Heuristic occupancy and non-occupancy detection in a lighting system

ABSTRACT

Disclosed herein is a lighting system configured to obtain an indicator data of a RF spectrum signal generated by a number of receivers at a number of times in an area. At each respective one of the number of times, for each respective one of the receivers, apply one of a plurality of heurist algorithm coefficients to each indicator data for the respective time, based on results of the applications of the coefficients to indicator data, generate an indicator data metric value for each of the indicator data for the respective time, and process the indicator data metric values to compute an output value. The lighting system is further configured to compare the output value at each of the plurality of times with a threshold to detect one of an occupancy condition or a non-occupancy condition in the area and control the light source in response to the detected one of the occupancy condition or the non-occupancy condition in the area at each of the number of times.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.15/840,694, filed Dec, 13. 2017, entitled “Heuristic Occupancy andNon-Occupancy Detection in a Lighting System with a Single Transmitterand Multiple Receivers.” This application is also related to U.S. patentapplication Ser. No. 15/840,827, filed Dec. 13, 2017, entitled“Heuristic Occupancy and Non-Occupancy Detection in a Lighting Systemwith Multiple Transmitters and a Single Receiver.”

BACKGROUND

In recent years, a number of systems and methods have been proposed foroccupancy detection within a particular area utilizing radio frequency(RF) based technologies. Examples of such systems include video sensormonitoring systems, radio frequency identification (RFID) systems,global positioning systems (GPS), and wireless communication systemsamong others. However, many of these systems have several disadvantages.For example, the video sensor monitoring system requires a considerablenumber of dedicated sensors that are expensive and the system requires alarge amount of memory for storing data. The RFID systems rely onoccupants carrying an RFID tag/card that can be sensed by the RFIDsystem to monitor the occupants. The GPS system uses orbiting satellitesto communicate with the terrestrial transceiver to determine a locationof the occupant in the area. However, such systems are generally lesseffective indoors or in other environments where satellite signals maybe blocked, reducing accuracy of detecting the occupant in the area.

Electrically powered artificial lighting has become ubiquitous in modernsociety. Since the advent of electronic light emitters, such as lightingemitting diodes (LEDs), for general lighting type illuminationapplication, lighting equipment has become increasingly intelligent withincorporation of sensors, programmed controller and networkcommunication capabilities. Automated control, particularly forenterprise installations, may respond to a variety of sensed conditions,such a daylight or ambient light level or occupancy. Commercial gradelighting systems today utilize special purpose sensors and relatedcommunications.

There also have been proposals to detect or count the number ofoccupants in an area based on effects on an RF signal received from atransmitter due to the presence of the occupant(s) in the area. These RFwireless communication systems generally detect an occupant in the areabased on change in signal characteristics of a data packet transmittedover the wireless network. However, an inaccurate detection of theoccupant in a region or a sub-area in the area can occur when multipletransmitters are transmitting the RF signals from multiple differentregions/sub-areas of the area.

SUMMARY

The examples disclosed herein improve over RF-based sensing technologiesby heuristically detecting one or more occupants in a space. In suchexamples, occupancy is sensed based on measurements of RF perturbationsin an area or space. An example machine learning algorithm involvesdetermining optimized heuristic algorithm coefficients associated withthe RF perturbations to provide occupancy sensing in the area at a time.The optimized heuristic algorithm coefficients are utilized in theexample machine learning algorithm to provide the occupancy sensing inthe area at real time. In one example, prior to the real time detection,learning occurs to optimize the coefficients, for example, prior toshipping of a product or as part of commissioning. In another example,learning occurs in real time operation, thus resulting in an on-goinglearning process to further optimize the coefficients.

Further example lighting system includes a light source and a pluralityof wireless communication transmitters for wireless radio frequency (RF)spectrum transmission in an area, including RF spectrum transmission ofat a plurality of times. The lighting system also includes a pluralityof wireless communication receivers configured to receive signals oftransmissions from each of the plurality of transmitters through thearea at the plurality of times. Each of the plurality of the receiversis configured to generate an indicator data of a signal characteristicof received an RF spectrum signal received from each of the transmittersat each of the plurality of times. The lighting system also includes acontrol module coupled to the light source and coupled to obtain theindicator data of RF spectrum signals generated at each of the pluralityof times from each of the plurality of receivers. At each respective oneof the plurality of times and for each respective one of the receivers,the control module is configured to apply one of a plurality of heuristalgorithm coefficients (coefficients) to the indicator data of signalsreceived from the transmitters, generated by the respective receiver,for the respective time, and based on results of the application of thecoefficients to the indicator data, generate an indicator data metricvalue for the indicator data generated by the respective receiver forthe respective time. At each respective one of the plurality of timesthe control module is also configured to process the indicator datametric values to compute an output value for the plurality of thereceivers, and compare the output value at the respective time with athreshold to detect one of a one of an occupancy condition or anon-occupancy condition in the area.

An example method includes obtaining, in a lighting system, an indicatordata generated at each of a plurality of times from each of a pluralityof receivers configured to receive radio frequency (RF) spectrum signalsfrom each of a plurality of RF transmitters in an area. At eachrespective one of the plurality of times in the lighting system, themethod also includes applying a plurality of heurist algorithmcoefficients (coefficients) to each indicator data from each of theplurality of receivers for the respective time, based on results of theapplications of the coefficients to indicator data, generating anindicator data metric value for each of the indicator data from each ofthe plurality of receivers for the respective time, and processing eachof the indicator data metric value for each of the indictor data tocompute an output value for the respective time. The method furtherincludes comparing the output value for the respective time with athreshold to detect one of a one of an occupancy condition or anon-occupancy condition in the area.

Additional objects, advantages and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing and the accompanying drawings or may be learned by productionor operation of the examples. The objects and advantages of the presentsubject matter may be realized and attained by means of themethodologies, instrumentalities and combinations particularly pointedout in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing figures depict one or more implementations in accordancewith the present teachings, by way of example only, not by way oflimitation. In the figures, like reference numerals refer to the same orsimilar elements.

FIG. 1A illustrates an example of a wireless topology of a lightingsystem with a single transmitter and multiple receivers.

FIG. 1B illustrates an example of a wireless topology of a lightingsystem with a single receiver and multiple transmitters.

FIG. 2A is a functional block diagram illustrating an example of aheuristic occupancy sensing system based on the wireless topology ofFIG. 1A in accordance with an implementation of a local control of alight source in a lighting system.

FIG. 2B is a functional block diagram illustrating an example of aheuristic occupancy sensing system based on the wireless topology ofFIG. 1A in accordance with an implementation of a local control of alight source in a lighting system.

FIG. 3 illustrates an example of a wireless topology of a lightingsystem with multiple transmitters and multiple receivers.

FIG. 4 is a functional block diagram depicting an example of a heuristicoccupancy sensing system based on the wireless topology of FIG. 3 inaccordance with an implementation of a local control of a light sourcein a lighting system.

FIG. 5 illustrates an example of a neural network for heuristicallydetermining an occupancy or non-occupancy condition in a lightingsystem.

FIG. 6 is a high-level flow chart illustration of an example of a methodfor heuristically determining an occupancy or non-occupancy condition.

FIG. 7 is a functional block diagram illustrating an example relating toa lighting system of networked devices that provide a variety oflighting capabilities and may implement RF-based occupancy sensing.

FIG. 8 is a block diagram of an example of a lighting device thatoperates in and communicates via the lighting system of FIG. 7.

FIG. 9 is a block diagram of an example of a wall switch type userinterface element that operates in and communicates via the lightingsystem of FIG. 7.

FIG. 10 is a block diagram of an example of a sensor type element thatoperates in and communicates via the lighting system of FIG. 7.

FIG. 11 is a block diagram of an example of a plug load controller typeelement that operates in and communicates via the lighting system ofFIG. 7.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent that the presentteachings may be practiced without such details. In other instances,well known methods, procedures, components, and/or circuitry have beendescribed at a relatively high-level, without detail, in order to avoidunnecessarily obscuring aspects of the present teachings.

Although there have been suggestions to control lighting based on RFwireless detection results, prior RF-based detection systems have notthemselves been integrated as part of a machine learning (ML) in alighting system of which the lighting operation are controlled as afunction of the detection.

There is also room for improvement in the RF wireless detectionalgorithms for lighting system control. For example, a ML algorithm inthe lighting system may enable a more rapid and real time response sothat an occupant entering a previously empty area perceives that thesystem instantly turns ON the light(s) in the area. As another example,the ML algorithm may offer improved detection accuracy, e.g. to reducefalse positives in detecting an occupant in the area.

Further, there is room for improvement for accurate detection of theoccupant in a sub-area among multiple different sub-areas of the area.False positives may occur when detecting an occupant in a specificsub-area when multiple transmitters are transmitting the RF signals frommultiple different sub-areas of the area. For example, a ML algorithmmay offer improved occupancy detection accuracy, e.g. to reduce falsepositives in detecting the occupant in the actual sub-area of interestin the facility.

The examples described below and shown in the drawings integrate RFwireless based ML occupancy/non-occupancy detection capabilities in oneor more lighting devices or into lighting devices and/or other elementsforming a lighting system. Examples of a detection system address someor all of the concerns noted above regarding rapid real time detectionof changes in occupancy/non-occupancy status and/or improved detectionperformance, such as reduction or even elimination of false positiveoccupancy detections. These advantages and possibly other advantages maybe more readily apparent from the detailed description below andillustration of aspects of the examples in the drawings.

Referring to FIG. 1A, an example of a wireless topology 101 of alighting system includes a single wireless communication transmitter(Tx) and a number of wireless communication receivers (Rx) in physicalspace/area 105. In one implementation, an indoor environment isdescribed, but it should be readily apparent that the systems andmethods described herein are operable in external environments as well.Specifically, in this example, the area 105 is a room. In oneimplementation, although, not shown, the area 105 may also includecorridors, additional rooms, hallways etc.

As illustrated in the example in FIG. 1A, the area 105 includes threeintelligent system nodes 132, 134, 136. Each such system node has anintelligence capability to transmit a signal or receive a signal andprocess data. In one example, at least one system node includes a lightsource and is configured as a lighting device. In another example, asystem node includes a user interface component and is configured as alighting controller. In another example a system node includes aswitchable power connector and is configured as a plug load controller.In a further example, a system node includes sensor detector and isconfigured as a lighting related sensor.

System node 132 includes a transmitter T1 and system nodes 134 and 136includes receivers R1 and R2 respectively. In one implementation, one ofthe occupancy condition and the non-occupancy condition in the area 105is detected according to a heuristic occupancy sensing procedure as willbe described below with respect to FIG. 2A.

In the wireless topology 101, the T1 in the area 105 transmits a RFspectrum (RF) signal for some number (plurality >1) of times. Thetransmission may be specifically for the occupancy detection. Each ofthe receivers R1-R2 receives the transmissions of the RF signal throughthe area 105 for each of the plurality of times from T1. Accordingly,each of the R1 and R2 is configured to detect a metric of the receivedRF, which the system (e.g. at one or more of the nodes) uses to detectone of an occupancy condition and a non-occupancy condition based on theRF spectrum signals received from the T1 in the area 105.

Referring to FIG. 2A, there is shown a functional block diagram of anexample of a heuristic occupancy sensing system 200 configured tofunction on a radio frequency (RF) wireless communication network inaccordance with an implementation of a local control of a light sourcein a lighting system. As illustrated, the heuristic occupancy sensingsystem 200 includes a lighting system (system) 202 disposed within thephysical space/area 105 such as a room, corridor, etc. as describedabove with respect to FIG. 1A. In one implementation, an indoorenvironment is described, but it should be readily apparent that thesystems and methods described herein are operable in externalenvironments as well.

In one implementation, the system 202 includes the three intelligentsystem nodes 132, 134 and 136 as described with respect to FIG. 1Aabove. As discussed above, each such system node has an intelligencecapability to transmit and receive data and process the data. Eachsystem node, for example, may include a receiver (R) and/or atransmitter (T) along with another component used in lightingoperations. In one example, a system node includes a light source and isconfigured as a lighting device. In another example, a system nodeincludes a user interface component and is configured as a lightingcontroller. In another example the system node includes a switchablepower connector and is configured as a plug load controller. In afurther example, a system node includes sensor detector and isconfigured as a lighting related sensor. The system node 132 includes aT1, and each of the system nodes 134 and 136 includes a R1 or R2respectively.

As described above, the Tx is configured to transmit RF signals and eachof the Rx is configured to receive signals from the Tx. In oneimplementation, the system 202 includes a light source 206, and a systemnode containing the source 206 or coupled to and operating together withthe source 206 is configured as lighting device. The lighting device,for example, may take the form of a lamp, light fixture, or otherluminaire that incorporates the light source, where the light source byitself contains no intelligence or communication capability, such as oneor more LEDs or the like, or a lamp (e.g. “regular light bulbs”) of anysuitable type. The light source 206 is configured to illuminate some orall of the area 105. In one example, each of some number of individuallight sources 206 to illuminate portion(s) or sub-area(s) of the area105. Typically, a lighting system will include one or more other systemnodes, such as a wall switch, a plug load controller, or a sensor.

In one implementation, the lighting system includes a control module 216coupled to the receivers R1 and R2. In one implementation, the controlmodule is coupled to the light source 206. In an alternateimplementation, the control module 216 is coupled to the light source206 via a network (not shown). In another alternate implementation, thecontrol module 216 is coupled to the lighting system 202 via a network(not shown). In one implementation, the control module 216 isimplemented in firmware of a processor configured to determine one of anoccupancy condition or a non-occupancy condition in the area 105,although other circuitry or processor-based implementations may be used.In one implementation, the control module 216 is implemented in firmwareof the processor in the R1 and/or R2.

In one implementation, the system 202 includes a controller 218 coupledto the control module 216. In one implementation the controller 218 maybe the same or an additional processor configured to control operationsof elements in the system 202 in response to determination of one of theoccupancy condition or the non-occupancy condition in the area 105. Forexample, in an alternate implementation, when the system 202 includes alight source 206, the controller 218 is configured to process a signalto control operation of the light source 206. In one alternateimplementation, the controller 218 is configured to turn ON the lightsource 206 upon an occupancy condition detected by the control module216. In one implementation, the controller 218 is configured to turn OFFthe light source 206 upon a non-occupancy condition detected by thecontrol module 216. In another implementation, upon the detection of theoccupancy or non-occupancy condition in the area 105, the controller 218may be configured to provide other control and management functions inthe area such as heating, ventilation and air conditioning (HVAC), heatmapping, smoke control, equipment control, security control, etc.instead of or in addition to control of the light source(s). In yetanother implementation, the controller 218 communicates the occupancycondition or non-occupancy condition to the lighting network via a datapacket. The data packet is received by one or more luminaires in thelighting network, which are configured to turn ON or OFF the lightsource(s) 206 based on the occupancy or the non-occupancy conditionrespectively provided in the data packet. The luminaire or another nodeon the lighting network may receive the packet and respond to provideautomation of other energy control, equipment control, operationalcontrol and management systems (e.g. HVAC, heat mapping, smoke control,equipment control and security control) in the area. Accordingly, theoccupancy sensing system 200 communicates the occupancy/non-occupancycondition with other networks. In another alternate implementation, thecontroller 218 is coupled to the lighting system 202 via a network (notshown). Accordingly, the heuristic occupancy sensing system 200 isconfigured to function on the RF wireless communication network inaccordance with an implementation of a global control of a light source,as well as other automation control of energy, equipment, operationaland management, as discussed above, of the area in a lighting system.

In one implementation, the system nodes typically include a processor,memory and programming (executable instructions in the form of softwareand/or firmware). Although the processor may be a separate circuitry(e.g. a microprocessor), in many cases, it is feasible to utilize thecentral processing unit (CPU) and associated memory of a micro-controlunit (MCU) integrated together with a transceiver in the form of asystem on a chip (SOC). Such an SOC can implement the wirelesscommunication functions as well as the intelligence (e.g. including anydetector or controller capabilities) of the system node.

In examples discussed in more detail later, system nodes often mayinclude both a transmitter and a receiver (sometimes referenced togetheras a transceiver), for various purposes. At times, such atransceiver-equipped node may use its transmitter as part of a heuristicoccupancy sensing operation; and at other times such atransceiver-equipped node may use its receiver as part of a heuristicoccupancy sensing operation. Such nodes also typically include aprocessor, memory and programming (executable instructions in the formof software and/or firmware). Although the processor may be a separatecircuitry (e.g. a microprocessor), in many cases, it is feasible toutilize the central processing unit (CPU) and associated memory of amicro-control unit (MCU) integrated together with physical circuitry ofa transceiver in the form of a system on a chip (SOC). Such an SOC canimplement the wireless communication functions as well as theintelligence (e.g. including any detector or controller capabilities) ofthe system node.

Although the system nodes 132, 134 and 136 of FIG. 2A illustrate animplementation of a single Tx and a single Rx in each of the nodes, thesystem 202 may include other implementations such as multiple Txs in oneor more nodes (see e.g. FIG. 2B). Also, FIG. 2A illustrates theimplementation of a single Rx in each of the nodes, the system 202 mayinclude other implementations such as multiple Rx in one or more nodes.Further, the system 202 may include one or more Tx and one or more Rx ineach of the nodes. In the illustrated implementation, the system 202includes a single lighting device with one source 206, however, thesystem 202 may include multiple lighting devices 206 a-206 n (see e.g.FIG. 7) including one or more Tx and one or more Rx.

For discussion of an initial example of a heuristic RF-based occupancysensing operation, assume that the system 202 includes just the elementsshown in FIG. 1A. In one example, each of the system nodes 132, 134 and136 includes the capabilities to communicate over two different RFbands, although the concepts discussed herein are applicable to devicesthat communicate with luminaires and other system elements via a singleRF band. Hence, in the dual band example, the Tx/Rx may be configuredfor sending and receiving various types of data signals over one band,e.g. for the RF detection leading to occupancy detection. The other bandmay be used or for pairing and commissioning messages over another bandand/or for communications related to detection of RF or higher leveloccupancy sensing functions, e.g. between receivers R1 and R2 and thecontroller 220 or the control module 216. For example, the Tx and Rx areconfigured as a 900 MHz transmitter and receiver for communication of avariety of system or user data, including lighting control data, forexample, commands to turn lights on/off, dim up/down, set scene (e.g., apredetermined light setting), and sensor trip events. Alternatively, theTx and Rx may be configured as a 2.4 GHz transmitter and receiver forBluetooth low energy (BLE) communication of various messages related tocommissioning and maintenance of a wireless lighting system.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 105, and because the system passively monitorssignal broadcasts in the area 105 at a plurality of times, the heuristicoccupancy detection functionality does not require (does not rely on)the occupants to carry any device.

At a high level, the T1 transmits a RF signal at a plurality of times.The transmission may be specifically for the occupancy detection. Insome cases, however, where the transmitter is in another lighting deviceor other lighting system element (e.g. a sensor or a wall switch), thetransmissions maybe regular lighting related communications, such asreporting status, sending commands, reporting sensed events, etc. Eachof the R1-R2 receives the transmissions of the RF signal from the T1through the area 105 during each of the plurality of times. Each of theR1-R2 generates an indicator data of one or more characteristics of thereceived RF signal at the plurality of times. Some of examples of thecharacteristics include but are not limited to received signal strengthindicator (RSSI) data, bit error rate, packet error rate, phase changeetc. or a combination of two or more thereof. The RSSI data representsmeasurements of signal strength of the received RF. The bit error rateis rate of incorrect bits in received RF signals versus total number ofbits in the transmitted RF signals. The packet error rate is rate ofincorrect packets in received RF signals versus total number of packetsthe transmitted RF signals. Phase change is a change of phase of areceived RF signal compared to previous reception of the RF signal(typically measured between the antennas spaced apart from each other).For the purpose of the present description, we use RSSI data as thecharacteristics of the RF signal for processing by each of the R1-R2 togenerate as the indicator data. Each of the R1-R2 measures the signalstrength of the received RF signal and generates the RSSI data based onthe signal strength. The signal strength of each of the RF signal isbased whether an occupant exists in a path between each of the T1 andR1-R2 in the area 105.

For each time, each of the receivers R1-R2 supplied the generatedindicator data of one or more characteristics of the received RF signalto the control module. In one implementation using RSSI as thecharacteristic of interest, the control module 216 obtains the generatedRSSI data at each of the plurality of times from the various receiversR1-R2 and utilizes a heuristic algorithm to determine one of anoccupancy condition or a non-occupancy condition in the area 105 asdescribed in greater detail herein below.

In one implementation that takes advantage of the machine learning (ML)capability of the heurist algorithm, the system 202 includes a trusteddetector 230, which provides a known value (similar to the “knownanswer” as discussed above). Input from the trusted detector 230 trusteddetector 230 to “learn” so as to improve performance. The trusteddetector 230 in the example may be a standard occupancy sensor, such aspassive infrared occupancy detector, a camera based occupancy sensingsystem, BLE signal sensor (i.e. detecting presence of a phone), manualoperation of lighting control (i.e. someone walking into a dark roomturning on lights), microphone signal, voice command (a la Alexa), andany other signal or sensor data that can establish the presence of aperson in the room. Specifically, the trusted detector 230 provides aknown occupancy value for an accurate occupancy condition in the area105 and a known non-occupancy value for an accurate non-occupancycondition in the area 105. In one implementation, the known occupancyvalue and the known non-occupancy value are pre-determined prior toheuristically determining one of an occupancy or non-occupancy detectionin the area 105.

In one implementation, the control module 216 obtains the indicator dataof the RF signal generated for multiple times (ta-tn) from each of theR1 and R2. The control module 216 applies one of a heuristic algorithmcoefficient (coefficient) among a set of heuristic algorithmcoefficients to each of the indicator data from each of the R1 and R2 togenerate an indicator data metric value for each of the indicator datafrom each of the R1 and R2 for the times ta-tn. Each coefficient amongthe set of coefficients may be randomly selected at an initial stage oftraining. In one implementation, a coefficient is a variable. In oneimplementation, a value of the coefficient applied to an indicator datafrom R1 is the same as the value of the coefficient applied to anotherindicator data that is from R2. In another implementation, a value of afirst coefficient applied to an indicator data from the R1 is differentfrom value of another (second) coefficient applied to another indicatordata from R2. In one implementation, the control module 216 processesthe indicator data metric values to compute an output value at each ofthe times ta-tn. In one implementation, the control module 216determines a relationship of the output value (detected one of anoccupancy or non-occupancy condition in the area) with the known value(one of an occupancy value or a non-occupancy value) generated by thetrusted detector for each of the ta-tn. Specifically, the control module216 compares the output value at each of the ta-tn with a threshold of aknown value, for example, an output of the trusted detector 230, todetect one of a one of an occupancy condition or a non-occupancycondition in the area as described in greater detail below. In oneimplementation, the system 202 includes a learning module 220 coupled tothe control module 216 to determine whether the set of coefficients areoptimized coefficients based on the relationship determined by thecontrol module 216 at the times ta-tn to detect an accurate detection ofthe occupancy or the non-occupancy condition in the area. In oneimplementation, upon determination, that the set of coefficients areoptimized coefficients, the control module 216 instructs the controlmodule 216 to utilize the optimized coefficients in real time, In oneimplementation, upon determination, that the set of coefficients areoptimized coefficients, the control module 216 instructs the controlmodule 216 to update one or more coefficients among the set ofcoefficients and utilize the updated one or more coefficients in a nexttime. The above implementations are described in greater detail below.

In one example, the known value is a known occupancy value at a time t1among the times ta-tn. In one implementation, the control module 216determines that the output value falls within the threshold of the knownoccupancy value. In one implementation, the learning module 220determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for occupancy condition. In oneimplementation, the learning module 220 instructs the control module 216to utilize the optimized coefficients to apply to each indicator dataamong the plurality of indicator data from each of the plurality ofreceivers for the time t1 to detect the occupancy condition in realtime. Accordingly, the control module 216 applies the optimizedcoefficients to determine the occupancy condition in real time. Inanother implementation, the control module 216 determines that theoutput value does not fall within the known occupancy value. Thelearning module 220 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients The learning module 220 instructs the control module 216 toutilize the updated set of coefficients in a next time. The controlmodule 216 applies the updated coefficients to corresponding indicatordata from each of the R1 and R2 to generate an updated indicator datametric value for each of the indicator data from each of the R1 and R2at the time t1. In one implementation, the control module 216 processeseach of the updated indicator data metric values to compute an updatedoutput value at t1. In one implementation, the control module 216determines that the updated output value at the time t1 falls within thethreshold of the known occupancy value. As such, the learning module 220determines that the updated set of coefficients are optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for occupancy condition in real time.In another implementation, the control module 216 determines that theupdated output value does not fall within the known occupancy value. Thecontrol module 216 and the learning module 220 repeats the above processfor t1 until the output value falls within the threshold of the knownoccupancy value to determine that the set of coefficients correspondingto the indicator data from each of the R1 and R2 are the optimizedcoefficients for the t1 among the ta-tn to accurately detect theoccupancy condition at real time. Accordingly, the control module 216applies the optimized coefficients to determine the occupancy conditionin real time.

In another example, the known value is a known non-occupancy value atthe time t1. In one implementation, the control module 216 determinesthat the output value falls within the threshold of the knownnon-occupancy value. In one implementation, the learning module 220determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for non-occupancy condition. In oneimplementation, the learning module 220 instructs the control module 216to utilize the optimized coefficients to apply to each indicator dataamong the plurality of indicator data from each of the plurality ofreceivers for the time t1 to detect the non-occupancy condition in realtime. Accordingly, the control module 216 applies the optimizedcoefficients to determine the non-occupancy condition in real time. Inanother implementation, the control module 216 determines that theoutput value does not fall within the known non-occupancy value. Thelearning module 220 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients The learning module 220 instructs the control module 216 toutilize the updated set of coefficients in a next time. The controlmodule 216 applies the updated coefficients to corresponding indicatordata from each of the R and R2 to generate an updated indicator datametric value for each of the indicator data from each of the R1 and R2at the time t1. In one implementation, the control module 216 processeseach of the updated indicator data metric values to compute an updatedoutput value at t1. In one implementation, the control module 216determines that the updated output value at the time t1 falls within thethreshold of the known non-occupancy value. As such, the learning module220 determines that the updated set of coefficients are optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for non-occupancy condition in realtime. In another implementation, the control module 216 determines thatthe updated output value does not fall within the known non-occupancyvalue. The control module 216 and the learning module 220 repeats theabove process for t1 until the output value falls within the thresholdof the known non-occupancy value to determine that the set ofcoefficients corresponding to the indicator data from each of the R1 andR2 are the optimized coefficients for the t1 among the ta-tn toaccurately detect the non-occupancy condition at real time. Accordingly,the control module 216 applies the optimized coefficients to determinethe occupancy condition in real time.

In one implementation, the output value is computed for each of theindicator data at each of the ta-tn and compared with the one of a knownoccupancy value or the known non-occupancy value to determine theoptimized coefficients for each of the ta-tn to detect an accurateoccupancy or non-occupancy condition in the area 105 of FIG. 1A at eachof the ta-tn. In one implementation, the optimized set of coefficientsfor each of the ta-tn are utilized by the control module 216 to detectone of an accurate occupancy and non-occupancy condition in the area 105of FIG. 1A at real time.

Referring to FIG. 1B, an example of a wireless topology 103 of alighting system includes a single wireless communication receiver (Rx)and a number of wireless communication transmitters (Txs) in physicalspace/area 105. In one implementation, an indoor environment isdescribed, but it should be readily apparent that the systems andmethods described herein are operable in external environments as well.Specifically, in this example, the area 105 is a room. In oneimplementation, although, not shown, the area 105 may also includecorridors, additional rooms, hallways etc. As illustrated in the examplein FIG. 1B, the area 105 includes three intelligent system nodes, out ofwhich two are Tx 132, and Tx 133, and one is the Rx 136. As discussedabove, each such system node has an intelligence capability to transmita signal or receive a signal and process data. In one example, at leastone system node includes a light source and is configured as a lightingdevice. In another example, a system node includes a user interfacecomponent and is configured as a lighting controller. In another examplea system node includes a switchable power connector and is configured asa plug load controller. In a further example, a system node includessensor detector and is configured as a lighting related sensor. In oneimplementation, one of the occupancy condition and the non-occupancycondition in the area 105 is detected according to a heuristic occupancysensing procedure as will be described below with respect to FIG. 2B.

In the wireless topology 101, the T1 and T2 in the area 105 transmits aRF spectrum (RF) signal for some number (plurality >1) of times. Thetransmission may be specifically for the occupancy detection. The R1receives the transmissions of the RF signals through the area 105 foreach of the plurality of times from T1 and T2. Accordingly, the R1 isconfigured to detect a metric of the received RF, which the system (e.g.at one or more of the nodes) uses to detect one of an occupancycondition and a non-occupancy condition based on the RF signals receivedfrom the T1 and the T2 in the area 105.

Referring to FIG. 2B, there is shown a functional block diagram of anexample of a heuristic occupancy sensing system 201 configured tofunction on a radio frequency (RF) wireless communication network inaccordance with an implementation of a local control of a light sourcein a lighting system. As illustrated, the heuristic occupancy sensingsystem 201 includes a lighting system (system) 203 disposed within thephysical space/area 105 such as a room, corridor, etc. as describedabove with respect to FIG. 1B. In one implementation, an indoorenvironment is described, but it should be readily apparent that thesystems and methods described herein are operable in externalenvironments as well.

In one implementation, the system 203 includes the three intelligentsystem nodes, as described with respect to FIG. 1B above. As discussedabove, each such system node has an intelligence capability to transmitand receive data and process the data. Each system node, for example,may include a receiver (R) and/or a transmitter (T) along with anothercomponent used in lighting operations. In one example, a system nodeincludes a light source and is configured as a lighting device. Inanother example, a system node includes a user interface component andis configured as a lighting controller. In another example the systemnode includes a switchable power connector and is configured as a plugload controller. In a further example, a system node includes sensordetector and is configured as a lighting related sensor. The system node134 includes a R1, and each of the system nodes 132 and 133 includes aT1 or T2 respectively.

As described above, each of the Tx is configured to transmit RF signalsand the Rx is configured to receive signals from each of the Tx. Similarto the system 202 in FIG. 2A, in one implementation, the system 203includes a light source 206, and a system node containing the source 206or coupled to and operating together with the source 206 is configuredas lighting device. The lighting device, for example, may take the formof a lamp, light fixture, or other luminaire that incorporates the lightsource, where the light source by itself contains no intelligence orcommunication capability, such as one or more LEDs or the like, or alamp (e.g. “regular light bulbs”) of any suitable type. The light source206 is configured to illuminate some or all of the area 105. In oneexample, each of some number of individual light sources 206 toilluminate portion(s) or a sub-area(s) of the area 105. Typically, alighting system will include one or more other system nodes, such as awall switch, a plug load controller, or a sensor.

Similar to the system 202 in FIG. 2A, in one implementation, thelighting system 203 also includes a control module 216. The controlmodule 216 is coupled to the R2 134. In one implementation, the controlmodule is coupled to the light source 206. In an alternateimplementation, the control module 216 is coupled to the light source206 via a network (not shown). In another alternate implementation, thecontrol module 216 is coupled to the lighting system 203 via a network(not shown). In one implementation, the control module 216 isimplemented in firmware of a processor configured to determine one of anoccupancy condition or a non-occupancy condition in the area 105,although other circuitry or processor-based implementations may be used.In one implementation, the control module 216 is implemented in firmwareof the processor in the R1.

Similar to the system 202 in FIG. 2A, in one implementation, the system203 includes a controller 218 coupled to the control module 216. In oneimplementation the controller 218 may be the same or an additionalprocessor configured to control operations of elements in the system 203in response to determination of one of the occupancy condition or thenon-occupancy condition in the area 105. For example, in an alternateimplementation, when the system 203 includes a light source 206, thecontroller 218 is configured to process a signal to control operation ofthe light source 206. In one alternate implementation, the controller218 is configured to turn ON the light source 206 upon an occupancycondition detected by the control module 216. In one implementation, thecontroller 218 is configured to turn OFF the light source 206 upon anon-occupancy condition detected by the control module 216. In anotherimplementation, upon the detection of the occupancy or non-occupancycondition in the area 105, the controller 218 is configured to provideother control and management functions in the area such as heating,ventilation and air conditioning (HVAC), heat mapping, smoke control,equipment control, security control, etc. In another implementation, thecontroller 218 communicates the occupancy condition or non-occupancycondition to the lighting network via a data packet. The data packet isreceived by one or more luminaires in the lighting network, which areconfigured to turn ON or OFF the light source(s) 206 and/or in theluminaire or another network node to provide automation of other energycontrol, equipment control, operational control and management systems(e.g. HVAC, heat mapping, smoke control, equipment control, securitycontrol) in the area 105 based on the occupancy or the non-occupancycondition respectively provided in the data packet. Accordingly, theoccupancy sensing system 201 communicates the occupancy/non-occupancycondition with other networks. In another alternate implementation, thecontroller 218 is coupled to the lighting system 203 via a network (notshown). Accordingly, the heuristic occupancy sensing system 201 isconfigured to function on the RF wireless communication network inaccordance with an implementation of a global control of a light sourceas well as other automation control of energy, equipment, operationaland management, as discussed above, of the area in a lighting system.

Although, FIG. 2B illustrates the implementation of a single Rx and asingle Tx in each of the nodes, the system 203 may include otherimplementations such as multiple Rx in one or more nodes and multiple Txin one or more nodes. Further, the system 203 may include one or more Txand one or more Rx in each of the nodes. In the illustratedimplementation, the system 203 includes a single lighting device withone source 206, however, the system 203 may include multiple lightingdevices 206 a-206 n (see e.g. FIG. 7) including one or more Tx and oneor more Rx.

For discussion of an initial example of a heuristic RF-based occupancysensing operation, assume that the system 203 includes just the elementsshown in FIG. 1B. In one example, each of the system nodes 132, 133 and134 includes the capabilities to communicate over two different RFbands, although the concepts discussed herein are applicable to devicesthat communicate with luminaires and other system elements via a singleRF band. Hence, in the dual band example, the Tx/Rx may be configuredfor sending and receiving various types of data signals over one band,e.g. for the RF detection leading to occupancy detection. The other bandmay be used or for pairing and commissioning messages over another bandand/or for communications related to detection of RF or higher leveloccupancy sensing functions, e.g. between receiver R1 and the controller220 or the control module 216. For example, the Tx and Rx are configuredas a 900 MHz transmitter and receiver for communication of a variety ofsystem or user data, including lighting control data, for example,commands to turn lights on/off, dim up/down, set scene (e.g., apredetermined light setting), and sensor trip events. Alternatively, theTx and Rx may be configured as a 2.4 GHz transmitter and receiver forBluetooth low energy (BLE) communication of various messages related tocommissioning and maintenance of a wireless lighting system.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 105, and because the system passively monitorssignal broadcasts in the area 105 at a plurality of times, the heuristicoccupancy detection functionality does not require (does not rely on)the occupants to carry any device.

At a high level, each of the T1 and T2 transmits a RF signal at aplurality of times. The transmission may be specifically for theoccupancy detection. In some cases, however, where the transmitter is inanother lighting device or other lighting system element (e.g. a sensoror a wall switch), the transmissions maybe regular lighting relatedcommunications, such as reporting status, sending commands, reportingsensed events, etc. The R receives the transmissions of the RF signalsfrom the T1 and the T2 through the area 105 during each of the pluralityof times. The R1 generates an indicator data of one or morecharacteristics of the received RF signal at the plurality of times. Asdiscussed above, some of examples of the characteristics include but arenot limited to received signal strength indicator (RSSI) data, bit errorrate, packet error rate, phase change etc. or a combination of two ormore thereof. For the purpose of the present description, we use RSSIdata as the characteristics of the RF signal for processing by the R1 togenerate as the indicator data. The R1 measures the signal strength ofthe received RF signals transmitted by T1 and T2 and generates the RSSIdata based on the signal strength of the RF signals transmitted by T1and T2. The signal strength of each of the RF signal is based whether anoccupant exists in a path between each of the T1 and R1 and/or T2 and R1in the area 105.

For each time, the R1 supplied the generated indicator data of one ormore characteristics of the received RF signal transmitted by the T1 andT2 to the control module. In one implementation using RSSI as thecharacteristic of interest, the control module 216 obtains the generatedRSSI data at each of the plurality of times from the R1 and utilizes aheuristic algorithm to determine one of an occupancy condition or anon-occupancy condition in the area 105 as described in greater detailherein below.

As discussed above, in one implementation that takes advantage of themachine learning (ML) capability of the heurist algorithm, the system203 also includes a trusted detector 230, which provides a known value(similar to the “known answer” as discussed above). Input from thetrusted detector 230 trusted detector 230 to “learn” so as to improveperformance. The trusted detector 230 in the example may be a standardoccupancy sensor, such as passive infrared occupancy detector, a camerabased occupancy sensing system, BLE signal sensor (i.e. detectingpresence of a phone), manual operation of lighting control (i.e. someonewalking into a dark room turning on lights), microphone signal, voicecommand (a la Alexa), and any other signal or sensor data that canestablish the presence of a person in the room. Specifically, thetrusted detector 230 provides a known occupancy value for an accurateoccupancy condition in the area 105 and a known non-occupancy value foran accurate non-occupancy condition in the area 105. In oneimplementation, the known occupancy value and the known non-occupancyvalue are pre-determined prior to heuristically determining one of anoccupancy or non-occupancy detection in the area 105.

In one implementation, the control module 216 obtains the indicator dataof the RF signals (transmitted by T1 and T2) generated for multipletimes (ta-tn) from the R1. The control module 216 applies one of aheuristic algorithm coefficient (coefficient) among a set of heuristicalgorithm coefficients to each of the indicator data from the R1 togenerate an indicator data metric value for each of the indicator datafrom R1 for the times ta-tn. Each coefficient among the set ofcoefficients may be randomly selected at an initial stage of training.In one implementation, coefficient is a variable. In one implementation,the control module 216 processes the indicator data metric values tocompute an output value at each of the times ta-tn. In oneimplementation, the control module 216 determines a relationship of theoutput value (detected one of an occupancy or non-occupancy condition inthe area) with the known value (one of an occupancy value or anon-occupancy value) generated by the trusted detector for each of theta-tn. Specifically, the control module 216 compares the output value ateach of the ta-tn with a threshold of a known value, for example, anoutput of the trusted detector 230, to detect one of a one of anoccupancy condition or a non-occupancy condition in the area asdescribed in greater detail below. In one implementation, the system 203includes a learning module 220 coupled to the control module 216 todetermine whether the set of coefficients are optimized coefficientsbased on the relationship determined by the control module 216 at thetimes ta-tn to detect an accurate detection of the occupancy or thenon-occupancy condition in the area. In one implementation, upondetermination, that the set of coefficients are optimized coefficients,the control module 216 instructs the control module 216 to utilize theoptimized coefficients in real time, In one implementation, upondetermination, that the set of coefficients are optimized coefficients,the control module 216 instructs the control module 216 to update one ormore coefficients among the set of coefficients and utilize the updatedone or more coefficients in a next time. The above implementations aredescribed in greater detail below.

In one example, the known value is a known occupancy value at a time t1among the times ta-tn. In one implementation, the control module 216determines that the output value falls within the threshold of the knownoccupancy value. In one implementation, the learning module 220determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for occupancy condition. In oneimplementation, the learning module 220 instructs the control module 216to utilize the optimized coefficients to apply to each indicator dataamong the plurality of indicator data from each of the plurality ofreceivers for the time t1 to detect the occupancy condition in realtime. Accordingly, the control module 216 applies the optimizedcoefficients to determine the occupancy condition in real time. Inanother implementation, the control module 216 determines that theoutput value does not fall within the known occupancy value. Thelearning module 220 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients The learning module 220 instructs the control module 216 toutilize the updated set of coefficients in a next time. The controlmodule 216 applies the updated coefficients to corresponding indicatordata from the R1 to generate an updated indicator data metric value foreach of the indicator data from the R1 at the time t1. In oneimplementation, the control module 216 processes each of the updatedindicator data metric values to compute an updated output value at t1.In one implementation, the control module 216 determines that theupdated output value at the time t1 falls within the threshold of theknown occupancy value. As such, the learning module 220 determines thatthe updated set of coefficients are optimized coefficients to be appliedto the indicator data for the time t1 to determine the accuratedetection for occupancy condition in real time. In anotherimplementation, the control module 216 determines that the updatedoutput value does not fall within the known occupancy value. The controlmodule 216 and the learning module 220 repeats the above process for t1until the output value falls within the threshold of the known occupancyvalue to determine that the set of coefficients corresponding to theindicator data from the R1 are the optimized coefficients for the t1among the ta-tn to accurately detect the occupancy condition at realtime. Accordingly, the control module 216 applies the optimizedcoefficients to determine the occupancy condition in real time.

In another example, the known value is a known non-occupancy value atthe time t1. In one implementation, the control module 216 determinesthat the output value falls within the threshold of the knownnon-occupancy value. In one implementation, the learning module 220determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for non-occupancy condition. In oneimplementation, the learning module 220 instructs the control module 216to utilize the optimized coefficients to apply to each indicator datafrom R1 for the time t1 to detect the non-occupancy condition in realtime. Accordingly, the control module 216 applies the optimizedcoefficients to determine the non-occupancy condition in real time. Inanother implementation, the control module 216 determines that theoutput value does not fall within the known non-occupancy value. Thelearning module 220 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients The learning module 220 instructs the control module 216 toutilize the updated set of coefficients in a next time. The controlmodule 216 applies the updated coefficients to corresponding indicatordata from the R to generate an updated indicator data metric value foreach of the indicator data from the R1 at the time t1. In oneimplementation, the control module 216 processes each of the updatedindicator data metric values to compute an updated output value at t1.In one implementation, the control module 216 determines that theupdated output value at the time t1 falls within the threshold of theknown non-occupancy value. As such, the learning module 220 determinesthat the updated set of coefficients are optimized coefficients to beapplied to the indicator data for the time t1 to determine the accuratedetection for non-occupancy condition in real time. In anotherimplementation, the control module 216 determines that the updatedoutput value does not fall within the known non-occupancy value. Thecontrol module 216 and the learning module 220 repeats the above processfor t1 until the output value falls within the threshold of the knownnon-occupancy value to determine that the set of coefficientscorresponding to the indicator data from R1 are the optimizedcoefficients for the t1 among the ta-tn to accurately detect thenon-occupancy condition at real time. Accordingly, the control module216 applies the optimized coefficients to determine the occupancycondition in real time.

In one implementation, the output value is computed for each of theindicator data at each of the ta-tn and compared with the one of a knownoccupancy value or the known non-occupancy value to determine theoptimized coefficients for each of the ta-tn to detect an accurateoccupancy or non-occupancy condition in the area 105 of FIG. 1B at eachof the ta-tn. In one implementation, the optimized set of coefficientsfor each of the ta-tn are utilized by the control module 216 to detectone of an accurate occupancy and non-occupancy condition in the area 105of FIG. 1B at real time.

Referring to FIG. 3, an example of a wireless topology 301 of a lightingsystem includes a number of wireless communication transmitters (Tx) anda number of wireless communication receiver (Rx) in physical space/area305. In one implementation, indoor environment is described, but itshould be readily apparent that the systems and methods described hereinare operable in external environments as well. Specifically, in thisexample, the area 305 includes a combination of a room 360, and ahallway 380. In one implementation, although, not shown, the area 305may also include corridors, additional rooms, additional hallways etc.In one implementation, although, not shown, the area 305 may alsoinclude corridors, additional rooms, hallways etc.

A wall 390 separates the room 360 from the hallway 380 with an opening392. As illustrated in the example in FIG. 1, the area 305 includes sixintelligent system nodes 332, 334, 336, 338, 340, and 342. Each suchsystem node has an intelligence capability to transmit a signal orreceive a signal and process data. In one example, at least one systemnode includes a light source and is configured as a lighting device. Inanother example, a system node includes a user interface component andis configured as a lighting controller. In another example a system nodeincludes a switchable power connector and is configured as a plug loadcontroller. In a further example a system node includes sensor detectorand is configured as a lighting related sensor.

In general, a heuristic algorithm with prior or ongoing training formachine learning, “learns” how to manipulate various inputs, possiblyincluding previously generated outputs, in order to generate current newoutputs. As part of this learning process, the algorithm receivesfeedback on prior outputs and possibly some other inputs. Then, themachine learning algorithm calculates parameters to be associated withthe various inputs (e.g. the previous outputs, feedback, etc.). Theparameters are then utilized by the machine learning to manipulate theinputs and generate the current outputs intended to improve some aspectof system performance in a desired manner. During the machine learningphase, the training data is the discrepancy between the outputs of apresent system and the outputs of a trusted system.

In a lighting system with occupancy detection, for example, the trainingdata may be the discrepancy between the outputs of an RF based detectionsystem operating in a user/consumer installation and a trusted occupancydetection system such as a standard occupancy sensor (e.g. such as asensor using passive infrared (PIR), a camera based system, BLE signalsensor (i.e. detecting presence of a phone), manual operation oflighting control (i.e. someone walking into a dark room turning onlights), microphone signal, voice command (a la Alexa), and any othersignal or sensor data that can establish the presence of a person in theroom). Machine learning techniques such as logical regression andartificial neural networks are applied to reduce the discrepancy, orexample, by optimizing one or more coefficients used in the real timeoccupancy/non-occupancy decision. Training can take place ahead of thetime (before product shipment/commissioning) or in the field as anon-going optimization to reduce false positives in detecting anoccupant.

An example may apply a “supervised learning” approach in which thesystem will be provided a “known answer” from a “trusted detector” andmachine learning is used to optimize the occupancy/non-occupancy detectalgorithm to minimize the difference between the system output and the“known answer.” A trusted detector may be a passive infrared occupancydetector, a camera, BLE signal sensor (i.e. detecting presence of aphone), manual operation of lighting control (i.e. someone walking intoa dark room turning on lights), microphone signal, voice command (a laAlexa), and any other signal or sensor data that can establish thepresence of a person in the room. The particular machine learningapproach can be one of decision tree or artificial neural net.

Learning can take place prior to shipping product or as part ofcommissioning after installation. In either such case, the system maynormally operate in the field without using the trusted detector in realtime.

Alternatively, a trusted detector can be installed with the system inthe field and utilized in real time, in which case, there may beon-going machine learning. For an ongoing learning implementation, thedata can be routed to a cloud, learning can take place on anothersystem, and then the improved algorithm (e.g. in the form of new nodeparameters in the case of a neural network) can be downloaded to theinstalled lighting system.

In one implementation, details of the heuristic algorithm and machinelearning are provided in more detail with respect to the example of FIG.3.

System nodes 332, 334, 136 and 138 are located in the room 360 and thesystem nodes 340 and 342 are located in the hallway 380. Each of thesystem nodes 332, 334 and 336 include transmitters T1, T2 and T3respectively and each of the system nodes 338, 340 and 342 includereceivers R1, R2 and R3 respectively. In one implementation, one of theoccupancy condition and the non-occupancy condition in the entire area305 and or a sub-area (for example, room 360) in the area 305 isdetected according to a ML occupancy sensing procedure as will bedescribed below with respect to FIG. 4.

In the wireless topology 301 each of the transmitters T1-T3 in the area305 transmits a RF spectrum (RF) signal for some number (plurality >1)of times. The transmissions from T1-T3 may be specifically for theoccupancy detection or for other lighting system communications. Each ofthe receivers R1-R3 in the area 305 receives the transmissions of the RFsignal through the area 305 for each of the plurality of times from eachof the multiple T1-T3. Logically, such a three transmitter-threereceiver arrangement provides nine T-R pairings for the analysis (eachof the three transmitters T1-T3 each paired logically with each of thethree receivers R1-R3). Accordingly, each of the R1-R3 is configured todetect a metric of the received RF, which the system (e.g. at one ormore nodes) uses to detect one of an occupancy/non-occupancy analysiscondition in its own sub-area (room 360 or the hallway 380) based onreceipt of the multiple RF signals received globally from the multipleT1-T3 in the area 305.

In one example, it is desired to determine an occupancy or non-occupancycondition in a sub-area such as room 360 of the area 305. Thus, forexample, a RF perturbation caused by a person in room 360 is detected bythe T1/R1 and T2/R2 (in the room 360), each of which generates a signalindicator data for the heuristic analysis. A person in the room 360 canalso trigger a response in the hallway 390 detected by the T1/R3 and/orT2/R3 in the hallway 380, but at a lower signal level. A signal levelthreshold may be used to reject the false positive in the hallway 390. Asimilar threshold approach may be implemented to reject the falsepositives at the nodes, i.e. T3/R1 and T3/R2 in the room 360 when aperson in the hallway 390 causes T3 to transmit RF signals detected byR1 and R2 in the room 360. A similar threshold approach may beimplemented to prevent false positives at the nodes i.e. T3/R3 in thehallway 390.

In one example, it is desired to determine an occupancy or non-occupancycondition in the room 180. The heuristic algorithm is configured toprocessing indicator data from R1 and R2 to detect one of an inaccurateoccupancy or inaccurate non-occupancy condition in the room 180 sinceeach of R1 and R2 receives RF signals not only from the T1 and the T2 inthe room 180 but also receives RF signals from the T3 in the hallway180. Accordingly, the heuristic algorithm is applied to allow processingof indicator data from the R1 and R2 in the room 160 to ignore/eliminatethe RF signals received from the R3, which are generated by the T3 dueto the presence of the occupants in the hallway 180 and/or multipathreturns of signals generated by the T1 and T2 in the room 180 butreceived due to or modified by the presence of occupants in the hallway180.

Referring to FIG. 4, there is shown a functional block diagram ofanother example of a heuristic occupancy sensing system 400 configuredto function on a radio frequency (RF) wireless communication network inaccordance with an implementation of a local control of a light sourcein a lighting system. As illustrated, the ML occupancy sensing system400 includes a lighting system (system) 402 disposed within the physicalspace/area 305 such as a room and a hallway etc. as described above withrespect to FIG. 3. In one implementation, an indoor environment isdescribed, but it should be readily apparent that the systems andmethods described herein are operable in external environments as well.

In one implementation, the system 402 includes the six intelligentsystem nodes 332, 334, 336, 338, 340, and 342 as described with respectto FIG. 3 above. As discussed above, each such system node has anintelligence capability to transmit and receive data and process thedata. Each system node, for example, may include a receiver (R) and/or atransmitter (T) along with another component used in lightingoperations. In one example, a system node includes a light source and isconfigured as a lighting device. In another example, a system nodeincludes a user interface component and is configured as a lightingcontroller. In another example the system node includes a switchablepower connector and is configured as a plug load controller. In afurther example, a system node includes sensor detector and isconfigured as a lighting related sensor. In the example of the wirelesstopology of FIG. 3, the system nodes 332, 334, and 336 include T1, T2and T3 respectively; and the system nodes 338, 340 and 342 include R1,R2 and R3 respectively.

As described above, the Tx is configured to transmit RF signals and eachof the Rx is configured to receive signals from each Tx. In oneimplementation, the system 402 includes a light source 406, and a systemnode containing the source 206 or coupled to and operation together withthe source 406 and is configured as lighting device. The lightingdevice, for example, may take the form of a lamp, light fixture, orother luminaire that incorporates the light source, where the lightsource by itself contains no intelligence or communication capability,such as one or more LEDs or the like, or a lamp (e.g. “regular lightbulbs”) of any suitable type. The light source 406 is configured toilluminate some or all of the area 405. In one example, each of somenumber of individual the light sources 406 to illuminate portion(s) orsub-area(s) of the area 405. Typically, a lighting system will includeone or more other system nodes, such as a wall switch, a plug loadcontroller, or a sensor.

In one implementation, the lighting system includes a control module 416coupled to the receivers R1, R2 and R3. In one example, the controlmodule 416 is coupled to each of the R1, R2 and R3 via 530 (not shown).In one implementation, the control module 416 is coupled to the lightsource 406. In one alternate implementation, the control module 416 iscoupled to the light source 406 via a network (not shown). In anotheralternate implementation, the control module 416 is coupled to thelighting system 402 via a network (not shown). In one implementation,the control module 416 is implemented in firmware of a processorconfigured to determine one of an occupancy condition or a non-occupancycondition in the area 305 or the sub-area (for example, room 360) in thearea 305, although other circuitry or processor based implementationsmay be used. In one implementation, the control module 216 isimplemented in firmware of the processor in or more of the R1, R2 or R3.

In one implementation, the system 402 includes a controller 418 coupledto the control module 416. In one implementation the controller 418 maybe the same or an additional processor configured to control operationsof elements in the system 402 in response to determination of one of theoccupancy condition or the non-occupancy condition in the area 305 or asub-area (for example, room 360) in the area 305. For example, in analternate implementation, when the system 402 includes a light source406, the controller 418 is configured to process a signal to controloperation of the light source 406. In one alternate implementation, thecontroller 418 is configured to turn ON the light source 406 upon anoccupancy condition detected by the control module 416. In one alternateimplementation, the controller 418 is coupled to the control module 416via a network (not shown). In one implementation, the controller 418 isconfigured to turn OFF the light source 406 upon a non-occupancycondition detected by the control module 416. In another implementation,upon the detection of the occupancy or non-occupancy condition in thearea 105, the controller 218 is configured to provide other control andmanagement functions in the area such as heating, ventilation and airconditioning (HVAC), heat mapping, smoke control, equipment control,security control, etc. In another implementation, the controller 418communicates the occupancy condition or non-occupancy condition to thelighting network via a data packet. The data packet is received by oneor more luminaires in the lighting network, which are configured to turnON or OFF the light source(s) 406 and/or in the luminaire or anothernetwork node to provide automation of other energy control, equipmentcontrol, operational control and management systems (e.g. HVAC, heatmapping, smoke control, equipment control, security control) in the area105 based on the occupancy or the non-occupancy condition respectivelyprovided in the data packet. Accordingly, the heuristic occupancysensing system 400 communicates the occupancy/non-occupancy conditionwith other networks. In another alternate implementation, the controller418 is coupled to the lighting system 402 via a network (not shown).Accordingly, the heuristic occupancy sensing system 400 is configured tofunction on the RF wireless communication network in accordance with animplementation of a global control of a light source, as well as otherautomation control of energy, equipment, operational and management, asdiscussed above, of the area in a lighting system.

In one implementation, the system nodes typically include a processor,memory and programming (executable instructions in the form of softwareand/or firmware). Although the processor may be a separate circuity(e.g. a microprocessor), in many cases, it is feasible to utilize thecentral processing unit (CPU) and associated memory of a micro-controlunit (MCU) integrated together with a transceiver in the form of asystem on a chip (SOC). Such an SOC can implement the wirelesscommunication functions as well as the intelligence (e.g. including anydetector or controller capabilities) of the system node.

In examples discussed in more detail later, system nodes often mayinclude both a transmitter and a receiver (sometimes referenced togetheras a transceiver), for various purposes. At times, such atransceiver-equipped node may use its transmitter as part of a heuristicoccupancy sensing operation; and at other times such atransceiver-equipped node may use its receiver as part of a heuristicoccupancy sensing operation. Such nodes also typically include aprocessor, memory and programming (executable instructions in the formof software and/or firmware). Although the processor may be a separatecircuity (e.g. a microprocessor), in many cases, it is feasible toutilize the central processing unit (CPU) and associated memory of amicro-control unit (MCU) integrated together with physical circuitry ofa transceiver in the form of a system on a chip (SOC). Such an SOC canimplement the wireless communication functions as well as theintelligence (e.g. including any detector or controller capabilities) ofthe system node.

Although the system nodes 332, 334, 336, 338, 340 and 342 of FIG. 3illustrates an implementation of a single Tx and a single Rx in each ofthe nodes, the system 402 may include other implementations such asmultiple Txs (see e.g. FIGS. 2A to 2C) in one or more nodes. Also, FIG.3 illustrates the implementation of a single Rx in each of the nodes,the system 202 may include other implementations such as multiple Rx(see e.g. FIGS. 2B to 2C) in one or more nodes. Further, in theillustrated implementation the system 402 includes a single lightingdevice with one source 406, however, the system 402 may include multiplelighting devices 406 a-406 n (see e.g. FIG. 7) including one or more Txand one or more Rx.

For discussion of an initial example of a heuristic RF-based occupancysensing operation, assume that the system 402 includes just the elementsshown in FIG. 3. In one example, each of the system nodes 332, 334, 336and 338 includes the capabilities to communicate over two different RFbands, although the concepts discussed herein are applicable to devicesthat communicate with luminaires and other system elements via a singleRF band. Hence, in the dual band example, the Tx/Rx may be configuredfor sending and receiving various types of data signals over one band,e.g. for the RF detection leading to occupancy detection. The other bandmay be used or for pairing and commissioning messages over another bandand/or for communications related to detection of RF or higher leveloccupancy sensing functions, e.g. between receivers R1 and R2 and thecontroller 420 or the control module 416. For example, the Tx and Rx areconfigured as a 900 MHz transmitter and receiver for communication of avariety of system or user data, including lighting control data, forexample, commands to turn lights on/off, dim up/down, set scene (e.g., apredetermined light setting), and sensor trip events. Alternatively, theTx and Rx may be configured as a 2.4 GHz transmitter and receiver forBluetooth low energy (BLE) communication of various messages related tocommissioning and maintenance of a wireless lighting system.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 305, and because the system passively monitorssignal broadcasts in the area 305 at a plurality of times, the heuristicoccupancy detection functionality does not require (does not rely on)the occupants to carry any device.

At a high level, the T1 transmits a RF spectrum (RF) signal at aplurality of times. The transmission may be specifically for theoccupancy detection. In some cases, however, where the transmitter is inanother lighting device or other lighting system element (e.g. a sensoror a wall switch), the transmissions maybe regular lighting relatedcommunications, such as reporting status, sending commands, reportingsensed events, etc. Each of the R1-R3 receives the transmissions of theRF signal from each of the T1-T3 through the area 305 for each of theplurality of times. Each of the R1-R3 generates an indicator data of oneor more characteristics of the received RF signal at the plurality oftimes. Some examples of the characteristics include but are not limitedto received signal strength indicator (RSSI) data, bit error rate,packet error rate, phase change etc. or a combination of two or morethereof. The RSSI data represents measurements of signal strength of thereceived RF. The bit error rate is rate of incorrect bits in received RFsignals versus total number of bits in the transmitted RF signals. Thepacket error rate is rate of incorrect packets in received RF signalsversus total number of packets the transmitted RF signals. Phase changeis a change of phase of a received RF signal compared to previousreception of the RF signal (typically measured between the antennasspaced apart from each other). For the purpose of the presentdescription, we use RSSI data as the characteristics of the RF signalfor processing by each of the R1-R3 to generate as the indicator data.Each of the R1-R3 measures the signal strength of the received RF signaland generates the RSSI data based on the signal strength. The signalstrength of each of the RF signal is based whether an occupant exists ina path between each of the T1-T3 and R1-R3 in the area 305.

For each time, each of the receivers R1-R3 supplied the generatedindicator data of one or more characteristics of the received RF signalto the control module. In one implementation using RSSI as thecharacteristic of interest, the control module 416 obtains the generatedRSSI data at each of the plurality of times from the various receiversR1-R3 and utilizes a heuristic algorithm to determine one of anoccupancy condition or a non-occupancy condition in the area 305 or thesub-area (for example, room 360) in the area 305 as described in greaterdetail herein below.

The control module 416 applies one of a heuristic algorithm coefficient(coefficient) among a set of heuristic algorithm coefficients to each ofthe indicator data from each of the R1-R3 to generate an indicator datametric value for each of the indicator data from each of the R1-R3 forthe times ta-tn. Each coefficient among the set of coefficients may berandomly selected at an initial stage of training. In oneimplementation, a set of coefficients are utilized to detect occupancyand non-occupancy condition in the entire area 305. In oneimplementation, a different set of coefficients are utilized to detectthe occupancy condition and the non-occupancy condition in the region(example, room 360) in the area 305. As such, the different set ofcoefficients are selected to reject false positives such as transmissionsignals from T3 and received signals from R3 that are not part of theroom 360. In one implementation, the heuristic algorithm is trainedusing the appropriate set of coefficients to detect the occupancy andnon-occupancy condition in the entire area 305 or the region of thearea, for example, the room 360. As discussed above, training can takeplace ahead of the time (before product shipment/commissioning) or inthe field as an on-going optimization to reduce false positives indetecting an occupant. Also as discussed above, the training is executedby the trusted detector. Accordingly, the occupancy and non-occupancydetection as discussed below with respect to the area may include theentire area 305 or a region (example room 360) of the entire area 305.

In one implementation, a coefficient is a variable. In oneimplementation, a value of a coefficient applied to an indicator datafrom one of the R1-R3 is the same as a value of a coefficient applied toanother indicator data that is from another one of the R1-R3. In anotherimplementation, a value of a first coefficient applied to an indicatordata from one of the R1-R3 is different from value of another (second)coefficient applied to another indicator data from another one of theR1-R3. In one implementation, the control module 416 processes theindicator data metric values to compute an output value at each of thetimes ta-tn. In one implementation, the control module 416 determines arelationship of the output value (detected one of an occupancy ornon-occupancy condition in the area) with the known value (one of anoccupancy value or a non-occupancy value) generated by the trusteddetector for each of the times ta-tn. Specifically, the control module416 compares the output value at each of the ta-tn with a threshold of aknown value, for example, an output of the trusted detector 420, todetect one of a one of an occupancy condition or a non-occupancycondition in the area as described in greater detail below. In oneimplementation, the system 402 includes a learning module 420 coupled tothe control module 416 to determine whether the set of coefficients areoptimized coefficients based on the relationship determined by thecontrol module 416 at times ta-tn to detect an accurate detection of theoccupancy or the non-occupancy condition in the area. In oneimplementation, upon determination, that the set of coefficients areoptimized coefficients, the control module 416 instructs the controlmodule 416 to utilize the optimized coefficients in real time. In oneimplementation, upon determination, that the set of coefficients areoptimized coefficients, the control module 416 instructs the controlmodule 416 to update one or more coefficients among the set ofcoefficients and utilize the updated one or more coefficients in a nexttime. The above implementations are described in greater detail below.

In one example, the known value is a known occupancy value at the timet1 among the times ta-tn. In one implementation, the control module 416determines that the output value falls within the threshold of the knownoccupancy value. In one implementation, the learning module 420determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for occupancy condition. In oneimplementation, the learning module 420 instructs the control module 416to utilize the optimized coefficients to apply to each indicator dataamong the plurality of indicator data from each of the plurality ofreceivers for the time t1 to detect the occupancy condition in realtime. Accordingly, the control module 416 applies the optimizedcoefficients to determine the occupancy condition in real time. Inanother implementation, the control module 416 determines that theoutput value does not fall within the known occupancy value. Thelearning module 420 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients. The learning module 420 instructs the control module 416to utilize the updated set of coefficients in a next time. The controlmodule 416 applies the updated coefficients to corresponding indicatordata from each of the R1-R3 to generate an updated indicator data metricvalue for each of the indicator data from each of the R-R3 at the timet1. In one implementation, the control module 416 processes each of theupdated indicator data metric values to compute an updated output valueat t1. In one implementation, the control module 416 determines that theupdated output value at the time t1 falls within the threshold of theknown occupancy value. As such, the learning module 420 determines thatthe updated set of coefficients are optimized coefficients to be appliedto the indicator data for the time t1 to determine the accuratedetection for occupancy condition in real time. In anotherimplementation, the learning module 416 determines that the updatedoutput value does not fall within the known occupancy value. The controlmodule 416 and the learning module 420 repeats the above process for t1until the output value falls within the threshold of the known occupancyvalue to determine that the set of coefficients corresponding to theindicator data from each of the R1-R3 are the optimized coefficients forthe t1 among the ta-tn to accurately detect the occupancy condition atreal time. Accordingly, the control module 416 applies the optimizedcoefficients to determine the occupancy condition in real time.

In another example, the known value is a known non-occupancy value atthe time t1. In one implementation, the control module 416 determinesthat the output value falls within the threshold of the knownnon-occupancy value. In one implementation, the learning module 420determines, that the set of coefficients are determined to be optimizedcoefficients to be applied to the indicator data for the time t1 todetermine the accurate detection for non-occupancy condition. In oneimplementation, the learning module 420 instructs the control module 416to utilize the optimized coefficients to apply to each indicator dataamong the plurality of indicator data from each of the plurality ofreceivers for the time t1 to detect the non-occupancy condition in realtime. Accordingly, the control module 416 applies the optimizedcoefficients to determine the non-occupancy condition in real time. Inanother implementation, the control module 416 determines that theoutput value does not fall within the known non-occupancy value. Thelearning module 420 determines that the set of coefficients are notoptimized coefficients and thus updates the one or more coefficientsamong the set of the coefficients to generate updated set ofcoefficients. The learning module 420 instructs the control module 416to utilize the updated set of coefficients. The control module 416applies the updated coefficients to corresponding indicator data fromeach of the R1 and R2 to generate an updated indicator data metric valuefor each of the indicator data from each of the R1 and R2 at the timet1. In one implementation, the control module 416 processes each of theupdated indicator data metric values to compute an updated output valueat t1. In one implementation, the control module 416 determines that theupdated output value at the time t1 falls within the threshold of theknown non-occupancy value. As such, the learning module 420 determinesthat the updated set of coefficients are optimized coefficients to beapplied to the indicator data for the time t1 to determine the accuratedetection for non-occupancy condition in real time. In anotherimplementation, the control module 416 determines that the updatedoutput value does not fall within the known non-occupancy value. Thecontrol module 416 and the learning module 420 repeats the above processfor t1 until the output value falls within the threshold of the knownnon-occupancy value to determine that the set of coefficientscorresponding to the indicator data from each of the R1-R3 are theoptimized coefficients for the t1 among the ta-tn to accurately detectthe non-occupancy condition at real time. Accordingly, the controlmodule 416 applies the optimized coefficients to determine the occupancycondition in real time.

In one implementation, the output value is computed for each of theindicator data at each of the ta-tn and compared with the one of a knownoccupancy value or the known non-occupancy value to determine theoptimized coefficients for each of the ta-tn to detect an accurateoccupancy or non-occupancy condition in the area 305 or the region (forexample, room 360) in the area 305 of FIG. 3 at each of the ta-tn. Inone implementation, the optimized set of coefficients for each of theta-tn are utilized by the control module 416 to detect one of anaccurate occupancy and non-occupancy condition in the area 305 or theregion (for example, room 360) in the area 305 of FIG. 3 at real time.

In one implementation, the control module 416 includes a logisticregression technique as a training method to determine one of anoccupancy or non-occupancy detection of the area 305 or the sub-area(for example, room 360) in the area 305 of FIG. 3. A logistic regressionis a statistical method for analyzing a dataset in which there are oneor more independent variables that determine an outcome. The goal oflogistic regression is to find the best fitting model to describe therelationship between the dichotomous characteristic of interest (outcomevariable) and a set of independent variables. Logistic regressiongenerates coefficients (and its standard errors and significance levels)of a formula to predict a logit transformation of the probability ofpresence of the characteristic of interest. In one implementation, theset of independent variables is the indicator data, for example, RSSIdata (R11, R12, R13, R21, R22, R23, R31, R32 and R33). In oneimplementation, heuristic algorithm coefficients (coefficients) are aset of coefficients x1-x9, the outcome is a binary output value of theformula and the characteristic of interest is one of occupant ornon-occupant in the area. Specifically, R1 is the RSSI data generated bythe R1 based on the RF signal received from T1, R12, is the RSSI datagenerated by the R1 based on the RF signal received from T2, R13 is theRSSI data generated by the R1 based on the RF signal received from T3,R21 is the RSSI data generated by the R2 based on the RF signal receivedfrom T1, R22 is the RSSI data generated by the R2 based on the RF signalreceived from T2, R23 is the RSSI data generated by the R3 based on theRF signal received from T3, R31 is the RSSI data generated by the R2based on the RF signal received from T1, R32, is the RSSI data generatedby the R3 based on the RF signal received from T2, and R33 is the RSSIdata generated by the R3 based on the RF signal received from T3.

In this example, the training includes applying a coefficient among aset of the coefficients x1-x9 to each of the RSSI data (R11, R12, R13,R21, R22, R23, R31, R32 and R33) generated at multiple times. In oneimplementation, during an initial stage of the training, each of thecoefficients among the set of coefficient x1-x9 are randomly selected.In one implementation, value of a coefficient in a set of coefficientsx1-x9 is different from the value of another coefficient in the set ofcoefficients. In one implementation, value of a coefficient in a set ofcoefficients x1-x9 is same as another coefficient in a set ofcoefficients. In one implementation, one or more of the coefficientsamong the set of coefficients x0-x9 are updated based on an output valueas described in greater detail below.

In one implementation, the RSSI data is analyzed by applying one of thecoefficients among the set of coefficients to each of the RSSI data atmultiple times. Specifically, each of the R11, R12, R13, R21, R22, R23,R31, R32 and R33 is multiplied by its corresponding coefficient x1-x9resulting in multiple product values (x1R11, x2R12, x3, R13, x4R21,x5R22, x6R23, x7R3, x8R32 and x9R33). In one implementation, an outputvalue of the logistic regression is generated for the set ofcoefficients by adding up all the product values and an independentcoefficient x0 to compute a single added value, compute an exponentvalue of this single added value, adding a value of 1 to the exponentvalue to compute an added exponent value and dividing a value of 1 withthis added exponent value to determine the output value as shown hereinbelow:

$\frac{1}{\begin{matrix}{1 + {\exp\left\lbrack {- \left( {x_{0} + {x_{1}R_{11}} + {x_{2}R_{12}} + {x_{3}R_{13}} + {x_{4}R_{21}} + {x_{5}R_{22}} + {x_{6}R_{23}} +} \right.} \right.}} \\\left. \left. {{x_{7}R_{31}} + {x_{8}R_{32}} + {x_{9}R_{33}}} \right) \right\rbrack\end{matrix}}$

In one implementation, the output value is computed for each of the RSSIdata at multiple times (ta-tn). In one implementation, each output valuecomputed at each time among the multiple times (ta-tn) is compared witha threshold of a true occupancy value and a true non-occupancy value. Atrue occupancy value or a non-occupancy value is a “known answer”computed from a trusted detector (e.g. a passive infrared occupancydetector, a camera, BLE signal sensor (i.e. detecting presence of aphone), manual operation of lighting control (i.e. someone walking intoa dark room turning on lights), microphone signal, voice command (a laAlexa), and any other signal or sensor data that can establish thepresence of a person in the room) for each of the times among themultiple times (ta-tn). A threshold for true occupancy value is anoccupancy threshold and a threshold for true non-occupancy value is anon-occupancy threshold. For example, the true occupancy value is 1 fora time t1 among the times ta-tn and the occupancy threshold is any valuethat is equal to 0.5 or is between 0.5 and 1 or equal to 1. Thus, anyoutput value that falls within the occupancy threshold for the time t1is considered to be an accurate detection of the occupancy condition inthe area 305 or the sub-area (for example, room 360) in the area 305 ofFIG. 3. In another example, the true non-occupancy value is 0 for a timeperiod t8 among the times ta-tn and a threshold value is any value thatis equal to 0 or is in between 0 and 0.5. Thus, any output value thatfalls within the non-occupancy threshold for the time t8 is consideredto be an accurate detection of the non-occupancy condition in the area305 or the sub-area (for example, room 360) in the area 305 of FIG. 3.

In one implementation, when the output value falls within the occupancythreshold at the time t1, the x0 and the coefficients in the x1-x9 areconsidered to be optimized coefficients and these optimized coefficientsare utilized in the logistic regression as described above to detect anoccupancy condition in the area 305 or the sub-area (for example, room360) in the area 305 at a real time. In one implementation, when theoutput value does not fall within the occupancy threshold at the timet1, one or more coefficients in the x1-x9 and/or the x0 are updatedusing a first gradient function as shown herein below:

${Xn} = {{Xn} - {\eta\;\frac{\partial C}{\partial{Xn}}}}$

Xn is the coefficient, n is the learning rate, C is the cost (loss)function. C is the difference between the computed output value and thetrue occupancy or non-occupancy value. C is minimized by taking thegradient with respect to the coefficients. In one implementation, theupdating of one or more of the x1-x9 and/or the x0, computing of theoutput data values are repeated until the output data value falls withinthe occupancy threshold occupancy at the time t1. In one implementation,upon determination of the output value falling within the occupancythreshold at the time t1, the corresponding updated x0 and the updatedone or more of x1-x9 are determined to be the optimized coefficients andare utilized in the logistic regression as described above to detect anoccupancy condition in the area 305 or the sub-area (for example, room360) in the area 305 at a real time.

In one implementation, when the output value falls within thenon-occupancy threshold at the time t8, the x0 and the coefficients inthe x1-x9 are considered to be optimized coefficients and theseoptimized coefficients are utilized in the logistic regression asdescribed above to detect a non-occupancy condition in the area 305 orthe sub-area (for example, room 360) in the area 305 at a real time. Inone implementation, when the output value does not fall within thenon-occupancy threshold at the time t8, one or more coefficients in thex1-x9 and/or the x0 are updated using the first gradient function asdescribed above. In one implementation, the updating of the one or moreof the x1-x9 and/or x0, and computing of the output data values arerepeated until the output data value falls within the non-occupancythreshold occupancy at the time t8. In one implementation, upondetermination of the output value falling within the non-occupancythreshold at the time t8, the corresponding updated x0 and the updatedone or more of x1-x9 are determined to be the optimized coefficients andare utilized in the logistic regression as described above to detect anon-occupancy condition in the area 305 or the sub-area (for example,room 360) in the area 305 at a real time.

In one implementation, the control module 416 includes a neural networkas a training method to determine one of an occupancy or non-occupancydetection of the area 305 or the sub-area (for example, room 360) in thearea 305 of FIG. 3. Referring to FIG. 5, there is shown an example of aneural network 500. The neural network 500 includes an input layer 502of input nodes 502 a-502 i, at least one middle layer 504 of middlenodes 504 a-504 j and an output node 510. Although, the middle layer 504includes ten nodes, it is known to one of ordinary skill that the middlelayer 504 may include any number of nodes, the number likely to belarger than the number of input nodes. Even though only one middle layeris shown, it known to one of ordinary skill in the art that more thanone middle layer of nodes may be implemented in the neural network 500.As shown, each of the input nodes 504 a-504 i is coupled to each of themiddle nodes 504 a-504 j and each of the middle nodes 504 a-504 j iscoupled to the output node 510. In one implementation, each of themiddle nodes 504 a-504 j in the middle layer 504 includes acorresponding bias constant ba-bi unique to that node. The biasconstants ba-bi are initially randomly assigned. In one implementation,each connection from each of the input nodes 502 a-502 i to each of themiddle nodes 504 a-504 j includes a corresponding weight (Wa-Wi) uniqueto the connection. The weights Wa-Wi are initially randomly assigned. Inone implementation, the bias constants ba-bi and the weights Wa-Wi arethe plurality of coefficients as described above.

The input layer of nodes 502 a-502 i includes the RSSI data R11, R12,R13, R21, R22, R23, R31, R32 and R33. Specifically, input node 502 aincludes R1, input node 502 b includes R12, input node 502 c includesR13, input node 502 d includes R21, input node 502 e includes R22, inputnode 502 f includes R23, input node 502 g includes R31, input node 502 hincludes R32, and input node 502 i includes R33. As such, number ofinput nodes in the input layer of nodes 502 a-502 i is equal to numberof RSSI data R11, R12, R13, R21, R22, R23, R31, R32 and R33. In oneimplementation, a forward propagation including propagation function andan activation function is executed in the neural network as describedherein below.

An output of each of the input nodes 502 a-502 i is an input to each ofthe middle nodes 504 a-504 i in the middle layer. In one implementation,the forward propagation includes a propagation function executed at eachof the middle nodes, 504 a-504 i to generate propagation functionvalues. Specifically, the propagation function is determined bymultiplying each of the RSSI data, R11, R12, R13, R21, R22, R23, R31,R32 and R33 with its corresponding weight (W) among the Wa-Wi and addedwith its corresponding bias constant (b) among the ba-bi of each of themiddle nodes 504 a-504 i resulting in a propagation value Za-Zi at eachof the middle nodes 504 a-504 i, which is summed together into a singlepropagation value Zj as shown below:

$z_{j}^{\iota} = {{\sum\limits_{k}{w_{jk}^{\iota}R_{k}^{\iota - 1}}} + b_{j}^{\iota}}$

The single propagation value Zj is fed into the activation functionexecuted in the output node 510 resulting in an output value, aj asshown herein below:a _(j) ^(l) =f(z _(j) ^(l))

In one implementation, the output value is computed for each of the RSSIdata at multiple times (ta-tn). In one implementation, each output valuecomputed at each time among the multiple times (ta-tn) is compared witha threshold of a true occupancy value and a true non-occupancy value. Atrue occupancy value or a non-occupancy value is a “known answer”computed from a trusted detector (e.g. passive infrared occupancydetector, a camera, BLE signal sensor (i.e. detecting presence of aphone), manual operation of lighting control (i.e. someone walking intoa dark room turning on lights), microphone signal, voice command (a laAlexa), and any other signal or sensor data that can establish thepresence of a person in the room) for each of the times among themultiple times (ta-tn). A threshold for true occupancy value is anoccupancy threshold and a threshold for true non-occupancy value is anon-occupancy threshold. For example, the true occupancy value is 1 fortime t1 among the times ta-tn and an occupancy threshold for the trueoccupancy value is 0.8 (i.e. 80%). Thus any output value at the time t1is equal to or greater than the occupancy threshold is considered to bean accurate detection of the occupancy condition in the area 305 or thesub-area (for example, room 360) in the area 305 of FIG. 3. In anotherexample, the true non-occupancy value is 0 for time t8 among the timesta-tn and a non-occupancy threshold of the true non-occupancy value is0.2 (i.e. 20%). Thus, any output value at the time t8 is at equal to orgreater than the non-occupancy threshold is considered to be an accuratedetection of the non-occupancy condition in the area 305 or the sub-area(for example, room 360) in the area 305 of FIG. 3.

In one implementation, the output value at the time t1 is determined tobe 0.9, which is compared with the true occupancy threshold of 0.8.Since the output value at the time t1 is greater than the thresholdvalue of 0.8, then the corresponding weights Wa-Wi and the biasconstants ba-bi are considered to be optimized coefficients and theseoptimized coefficients are utilized in the forward propagation asdescribed above to detect an occupancy condition in the area 305 or thesub-area (for example, room 360) in the area 305 at real time. Inanother implementation, the output value at the time t1 is determined tobe 0.6, which is less than the occupancy threshold of 0.8, accordingly,one or more weights Wa-Wi may be updated using a second gradient descentfunction as shown below:

$w_{jk}^{\prime} = {w_{jk} - {\eta\;\frac{\partial C}{\partial w_{jk}}}}$

Further, one or more bias constants ba-bi may be updated using the thirdgradient descent function as shown herein below:

$b_{j}^{\prime} = {b_{j} - {\eta\;\frac{\partial C}{\partial b_{j}}}}$

W is the weight, b is the bias constant, n is the learning rate, C isthe cost (loss) function. C is the difference between the computedoutput value and the true occupancy or non-occupancy value. C isminimized by taking the gradient with respect to the coefficients. Inone implementation, a backward propagation function is applied to theneural network 500 using the one or more updated values of the weights,Wa-Wi and/or the one or more updated bias constants ba-bi. In oneimplementation, the backward propagation function includes providing theone or more updated values of the weights Wa-Wi and/or one or moreupdated bias constants ba-bi at the output node 510 and then cascadingbackwards towards the input node 502 by applying the one or more updatedweights Wa-Wi and/or the one or more updated values of the biasconstants ba-bi cascade backwards at each of the corresponding middlenodes 504 a-504 j in the middle layer 504 (including any additionalmiddle nodes in additional middle layers not shown).

In one implementation, an updated output value is generated for the t1with the one or more updated weights Wa-Wi and/or the one or moreupdated bias constants ba-bi using the forward propagation as describedabove. In one implementation, the updating of Wa-Ai using the secondgradient function and/or of the ba-bi using the third gradient functionas described above, backward propagation and the forward propagation arerepeated until the output data value falls within the occupancythreshold occupancy at the time t1. In one implementation, upondetermination of the output value falling within the occupancy thresholdat the time t1, the corresponding updated Wa-Wi and/or the updated ba-biare utilized in the forward propagation as described above to detect anoccupancy condition in the area 305 or the sub-area (for example, room360) in the area 305 at a real time.

Referring back to the example, above, in one implementation, when theoutput value at the time t8 is determined to be 0.2, which is comparedwith the true non-occupancy threshold of 0.2. Since, the output value of0.2 is equal to or less than the true occupancy threshold of 0.2, thecorresponding Wa-Wi and the bias constants ba-bi are considered to beoptimized coefficients and these optimized coefficients are utilized inthe forward propagation as described above to detect a non-occupancycondition in the area 305 or the sub-area (for example, room 360) in thearea 305 at real time. In another implementation, when the output valueat the time t8, is determined to be 0.4, which is less than the truenon-occupancy threshold of 0.2, than one or more weights Wa-Wi and/orone or more bias constants ba-bi are updated using the above second andthird gradient functions respectively as discussed above. In oneimplementation, the updating of the Wa-Wi and/or the ba-bi, the backwardpropagation and the forward propagation re repeated until the outputdata value falls within the non-occupancy threshold at the time, t8. Inone implementation, upon determination of the output value fallingwithin the non-occupancy threshold at the time t8, the corresponding oneor more updated Wa-Wi and/or one or more updated ba-bi are utilized inthe forward propagation as described above to detect a non-occupancycondition in the area 305 or the sub-area (for example, room 360) in thearea 305 at a real time.

FIG. 6 illustrates an example of a flowchart of a method 600 forheuristic detection of an occupancy and non-occupancy condition formultiple times in area 105 of a lighting system either of FIGS. 2A, 2Bor the area 305 or the sub-area (for example, room 360) in the area 305of the lighting system of FIG. 4. As discussed above, the lightingsystem (system) is disposed within a physical space/area such as a room,corridor, hallway, or doorway. In one implementation, indoor environmentis described, but it is known to one of ordinary skill that the systemsand methods described herein are operable in external environments aswell. In one implementation, the method 600 is implemented by thecontrol module 216 and the learning module 220 of FIG. 2A or FIG. 2B. Inone implementation, the method 600 is implemented by the control module416 and the learning module 420 of FIG. 4.

At block 602, an indicator data generated at each of the plurality oftimes from each of the plurality of receivers configured to receive RFspectrum (RF) signals from each of the plurality of RF transmitters inan area is obtained. As discussed above, some of the characteristicsinclude but are not limited to received signal strength indicator (RSSI)data, bit error rate, packet error rate, phase change etc. or acombination of two or more thereof. At block 604, at each respective oneof the plurality of times, a coefficient among a set of coefficients isapplied to each of the indicator data from each of the plurality ofreceivers for the respective time. In one implementation, during theinitial stage of the training, each of the coefficients among the set ofcoefficients are randomly selected. At block 606, at each respective oneof the plurality of times, generate an indicator data metric value foreach of the indicator data from each of the plurality of receivers forthe respective time based on results of the applications of thecoefficients to the indicator data. At block 608, at each respective oneof the plurality of times, process each of the indicator data metricvalue for each of the indicator data to compute an output value for therespective time. At block 610, at each respective one of the pluralityof times, the output value is compared with a threshold to detect one ofan occupancy condition or a non-occupancy condition in the area. In oneimplementation, a threshold is a threshold of the known occupancy valuefor the occupancy condition. In another implementation, a threshold ofthe known non-occupancy value for the non-occupancy condition. At block612, at each of the respective one of the plurality of times, arelationship is determined of the detected one of the occupancycondition or the non-occupancy condition in the area with a knownoccupancy value for the occupancy condition or a known non-occupancyvalue for the non-occupancy condition during the respective one of theplurality of times. At block 614, at each of the respective one of theplurality of times, it is determined whether the set of coefficients areoptimized coefficients based on the determined relationship during theplurality of times. At block 616, at each of the respective one of theplurality of times, decision is made whether the set of coefficients arethe optimized coefficients. When at block 616, it is determined that theset of coefficients are optimized coefficients, then at step 618, theoptimized coefficients are utilized to apply to each indicator data forthe detection of the occupancy condition or the non-occupancy conditionin the area at a real time. When at block 616, it is determined that theset of coefficients are not optimized coefficients, then at step 620, ateach of the respective one of the plurality of times, one or more of theset of coefficients are updated to generate an updated set ofcoefficients. In one implementation, the method is repeated from block604 for the updated set of coefficients until it is determined that theupdated set of coefficients are optimized coefficients to detect anaccurate occupancy or non-occupancy condition at each respective one ofthe plurality of times.

FIG. 7 is a functional block diagram illustrating an example relating toa system of a wireless networked devices that provide a variety oflighting capabilities and may implement RF-based occupancy sensing. Thewireless networked devices also provide ng communications in support oflighting functions such as turning lights on/off, dimming, set scene, orsensor trip events and may implement RF-based occupancy sensing. Itshould be understood that the term “lighting control device” means adevice that includes a controller (Control/XCVR module or micro-controlunit) that executes a lighting application for communication over awireless lighting network communication band, of control and systemsoperations information during control network operation over thelighting network communication band.

A lighting system 702 may be designed for indoor commercial spaces,although the system may be used in outdoor or residential settings. Asshown, system 702 includes a variety of lighting control devices, suchas a set of lighting devices (a.k.a. luminaires) 104 a-104 n (lightingfixtures), a set of wall switch type user interface component (a.k.a.wall switches) 720 a-720 n, a plug load controller type element (a.k.a.plug load controller) 730 and a sensor type element (a.k.a. sensor) 735.Daylight, ambient light, or audio sensors may embedded in lightingdevices, in this case luminaires 704 a-704 n. RF wireless occupancysensing as described above is implemented in one or more of theluminaires 704 a-704 n to enable occupancy/non-occupancy based controlof the light sources. One or more luminaires may exist in a wirelessnetwork 750, for example, a sub-GHz or Bluetooth (e.g. 2.4 GHz) networkdefined by an RF channel and a luminaire identifier.

The wireless network 750 may use any available standard technology, suchas WiFi, Bluetooth, ZigBee, etc. An example of a lighting system using awireless network, such as Bluetooth low energy (BLE), is disclosed inpatent application publication US20160248506 A1 entitled “System andMethod for Communication with a Mobile Device Via a Positioning SystemIncluding RF Communication Devices and Modulated Beacon Light Sources,”the entire contents of which are incorporated herein by reference.Alternatively, the wireless network may use a proprietary protocoland/or operate in an available unregulated frequency band, such as theprotocol implemented in nLight® Air products, which transport lightingcontrol messages on the 900 MHz band (an example of which is disclosedin U.S. patent application Ser. No. 15/214,962, filed Jul. 20, 2016,entitled “Protocol for Lighting Control Via a Wireless Network,” theentire contents of which are incorporated herein by reference). Thesystem may support a number of different lighting control protocols, forexample, for installations in which consumer selected luminaires ofdifferent types are configured for a number different lighting controlprotocols.

The system 702 also includes a gateway 752, which engages incommunication between the lighting system 702 and a server 705 through anetwork such as wide area network (WAN) 755. Although FIG. 7 depictsserver 705 as located off premises and accessible via the WAN 755, anyone of the luminaires 704 a-704 n, for example are configured tocommunicate one of a occupancy detection or a non-occupancy detection inan area to devices such as the server 705 or even a laptop 706 locatedoff premises.

The lighting control 702 can be deployed in standalone or integratedenvironments. System 702 can be an integrated deployment, or adeployment of standalone groups with no gateway 752. One or more groupsof lighting system 702 may operate independently of one another with nobackhaul connections to other networks.

Lighting system 702 can leverage existing sensor and fixture controlcapabilities of Acuity Brands Lighting's commercially available nLight®wired product through firmware reuse. In general, Acuity BrandsLighting's nLight® wired product provides the lighting controlapplications. However, the illustrated lighting system 704 includes acommunications backbone and includes model—transport, network, mediaaccess control (MAC)/physical layer (PHY) functions.

Lighting control 702 may comprise a mix and match of various indoorsystems, wired lighting systems (nLight® wired), emergency, and outdoor(dark to light) products that are networked together to form acollaborative and unified lighting solution. Additional control devicesand lighting fixtures, gateway(s) 750 for backhaul connection, time synccontrol, data collection and management capabilities, and interoperationwith the Acuity Brands Lighting's commercially available SensorViewproduct may also be provided.

FIG. 8 is a block diagram of a lighting device (in this example, aluminaire) 804 that operates in and communicates via the lighting system702 of FIG. 7. Luminaire 804 is an integrated light fixture thatgenerally includes a power supply 805 driven by a power source 800.Power supply 805 receives power from the power source 800, such as an ACmains, battery, solar panel, or any other AC or DC source. Power supply805 may include a magnetic transformer, electronic transformer,switching converter, rectifier, or any other similar type of circuit toconvert an input power signal into a power signal suitable for luminaire804.

Luminaire 804 furthers include an intelligent LED driver circuit 810,control/XCVR module 815, and a light emitting diode (LED) light source820. Intelligent LED driver circuit 810 is coupled to LED light source820 and drives that LED light source 820 by regulating the power to LEDlight source 820 by providing a constant quantity or power to LED lightsource 320 as its electrical properties change with temperature, forexample. The intelligent LED driver circuit 810 includes a drivercircuit that provides power to LED light source 820 and a pilot LED 817.The pilot LED 817 may be included as part of the control/XCVR module315. Intelligent LED driver circuit 810 may be a constant-voltagedriver, constant-current driver, or AC LED driver type circuit thatprovides dimming through a pulse width modulation circuit and may havemany channels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 810is manufactured by EldoLED.

LED driver circuit 810 can further include an AC or DC current source orvoltage source, a regulator, an amplifier (such as a linear amplifier orswitching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 810outputs a variable voltage or current to the LED light source 820 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

Control/XCR module 815 includes power distribution circuitry 825 and amicro-control unit (MCU) 830. As shown, MCU 830 is coupled to LED drivercircuit 810 and controls the light source operation of the LED lightsource 820. MCU 830 includes a memory 322 (volatile and non-volatile)and a central processing unit (CPU) 823. The memory 822 includes alighting application 827 (which can be firmware) for both occupancydetection and lighting control operations. The power distributioncircuitry 825 distributes power and ground voltages to the MCU 830,wireless transmitter 808 and wireless receiver 810, to provide reliableoperation of the various circuitry on the sensor/control module 815chip.

Luminaire 804 also includes a wireless radio communication interfacesystem configured for two way wireless communication on at least oneband. Optionally, the wireless radio communication interface system maybe a dual-band system. It should be understood that “dual-band” meanscommunications over two separate RF bands. The communication over thetwo separate RF bands can occur simultaneously (concurrently); however,it should be understood that the communication over the two separate RFbands may not actually occur simultaneously.

In our example, luminaire 804 has a radio set that includes radiotransmitter 808 as well as a radio receiver 810, together forming aradio transceiver. The wireless transmitter 808 transmits RF signals onthe lighting network. This wireless transmitter 808 wirelesscommunication of control and systems operations information, duringluminaire operation and during transmission over the first wirelesscommunication band. The wireless receiver carries out receiving of theRF signals from other system elements on the network and generating RSSIdata based on signal strengths of the received RF signals. If provided(optional) another transceiver (Tx and Rx) may be provided, for example,for point-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the luminaire 804 may have a radio set forming a secondtransceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The RF spectrum or “radio spectrum” is a non-visible part of theelectromagnetic spectrum, for example, from around 3 MHz up toapproximately 3 THz, which may be used for a variety of communicationapplications, radar applications, or the like. In the discussions above,the RF transmitted and received for network communication, e.g. Wifi,BLE, Zigbee etc., was also used for occupancy detection functions, inthe frequencies bands/bandwidths specified for those standard wirelessRF spectrum data communication technologies. In another implementation,the transceiver is an ultra-wide band (also known as UWB, ultra-wideband and ultraband) transceiver. UWB is a radio technology that can usea very low energy level for short-range, high-bandwidth communicationsover a large portion of the radio spectrum. UWB does not interfere withconventional narrowband and carrier wave transmission in the samefrequency band. Ultra-wideband is a technology for transmittinginformation spread over a large bandwidth (>500 MHz) and under certaincircumstances be able to share spectrum with other users.

Ultra-wideband characteristics are well-suited to short-distanceapplications, such as short-range indoor applications. High-data-rateUWB may enable wireless monitors, the efficient transfer of data fromdigital camcorders, wireless printing of digital pictures from a camerawithout the need for a personal computer and file transfers betweencell-phone handsets and handheld devices such as portable media players.UWB may be used in a radar configuration (emitter and deflectiondetection at one node) for real-time location systems and occupancysensing/counting systems; its precision capabilities and low power makeit well-suited for radio-frequency-sensitive environments. Anotherfeature of UWB is its short broadcast time. Ultra-wideband is also usedin “see-through-the-wall” precision radar-imaging technology, precisiondetecting and counting occupants (between two radios), precisionlocating and tracking (using distance measurements between radios), andprecision time-of-arrival-based localization approaches. It isefficient, with a spatial capacity of approximately 1013 bit/s/m². Inone example, the UWB is used as the active sensor component in anautomatic target recognition application, designed to detect humans orobjects in any environment.

The MCU 830 may be a system on a chip. Alternatively, a system on a chipmay include the transmitter 808 and receiver 810 as well as thecircuitry of the MCU 830.

As shown, the MCU 830 includes programming in the memory 822. A portionof the programming configures the CPU (processor) 823 to detect one ofan occupancy or non-occupancy condition in an area in the lightingnetwork, including the communications over one or more wirelesscommunication. The programming in the memory 822 includes a real-timeoperating system (RTOS) and further includes a lighting application 827which is firmware/software that engages in communications withcontrolling of the light source based on one of the occupancy ornon-occupancy condition detected by the CPU 823. The lightingapplication 827 programming in the memory 822 carries out lightingcontrol operations over the lighting network 750 of FIG. 7. Theprogramming for the detection of an occupancy or non-occupancy conditionin the area may be implemented as part of the RTOS, as part of thelighting application 827, as a standalone application program, or asother instructions in the memory.

FIG. 9 is a block diagram of a wall type user interface element 915 thatoperates in and communicates via the lighting system 702 of FIG. 7. Walltype user interface (UI) element (UI element) is an integrated wallswitch that generally includes a power supply 905 driven by a powersource 900. Power supply 905 receives power from the power source 900,such as an AC mains, battery, solar panel, or any other AC or DC source.Power supply 905 may include a magnetic transformer, electronictransformer, switching converter, rectifier, or any other similar typeof circuit to convert an input power signal into a power signal suitablefor the UI element 915.

UI element 915 furthers includes an intelligent LED driver circuit 910,coupled to LED (s) 920 and drives that LED light source (LED) 920 byregulating the power to LED 820 by providing a constant quantity orpower to LED 920 as its electrical properties change with temperature,for example. The intelligent LED driver circuit 910 includes a drivercircuit that provides power to LED 920 and a pilot LED 917. IntelligentLED driver circuit 910 may be a constant-voltage driver,constant-current driver, or AC LED driver type circuit that providesdimming through a pulse width modulation circuit and may have manychannels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 910is manufactured by EldoLED.

LED driver circuit 910 can further include an AC or DC current source orvoltage source, a regulator, an amplifier (such as a linear amplifier orswitching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 910outputs a variable voltage or current to the LED light source 920 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

The UI element 915 includes power distribution circuitry 925 and amicro-control unit (MCU) 930. As shown, MCU 930 is coupled to LED drivercircuit 910 and controls the light source operation of the LED 920. MCU930 includes a memory 922 (volatile and non-volatile) and a centralprocessing unit (CPU) 923. The memory 922 includes a lightingapplication 927 (which can be firmware) for both occupancy detection andlighting control operations. The power distribution circuitry 925distributes power and ground voltages to the MCU 930, wirelesstransmitter 908 and wireless receiver 910, to provide reliable operationof the various circuitry on the UI element 915 chip.

The UI element 915 also includes a wireless radio communicationinterface system configured for two way wireless communication on atleast one band. Optionally, the wireless radio communication interfacesystem may be a dual-band system. It should be understood that“dual-band” means communications over two separate RF bands. Thecommunication over the two separate RF bands can occur simultaneously(concurrently); however, it should be understood that the communicationover the two separate RF bands may not actually occur simultaneously.

In our example, the UI element 915 has a radio set that includes radiotransmitter 908 as well as a radio receiver 910 together forming a radiotransceiver. The wireless transmitter 908 transmits RF signals on thelighting network. This wireless transmitter 908 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationband. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the UI element 915 may have a radio set forming a secondtransceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 930 may be a system on a chip. Alternatively, a system on a chipmay include the transmitter 908 and receiver 910 as well as thecircuitry of the MCU 930.

As shown, the UI element 915 includes a drive/sense circuitry 935, suchas an application firmware, drives the occupancy, audio, and photosensor hardware. The drive/sense circuitry 935 detects state changes(such as change of occupancy, audio or daylight sensor or switch to turnlighting on/off, dim up/down or set scene) via switches 965, such as adimmer switch, set scene switch. Switches 965 can be or include sensors,such as infrared sensors for occupancy or motion detection, anin-fixture daylight sensor, an audio sensor, a temperature sensor, BLEsignal sensor (i.e. detecting presence of a phone), manual operation oflighting control (i.e. someone walking into a dark room turning onlights), microphone signal, voice command (a la Alexa), and any othersignal or sensor data that can establish the presence of a person in theroom. Switches 965 may be based on Acuity Brands Lighting's commerciallyavailable xPoint® Wireless ES7 product.

Also, as shown, the MCU 930 includes programming in the memory 922. Aportion of the programming configures the CPU (processor) 923 to detectone of an occupancy or non-occupancy condition in an area in thelighting network, including the communications over one or more wirelesscommunication bands. The programming in the memory 922 includes areal-time operating system (RTOS) and further includes a lightingapplication 927 which is firmware/software that engages incommunications with controlling of the light source based on one of theoccupancy or non-occupancy condition detected by the CPU 923. As shown,a drive/sense circuitry detects a state change event. The lightingapplication 927 programming in the memory 922 carries out lightingcontrol operations over the lighting system 702 of FIG. 7. Theprogramming for the detection of an occupancy or non-occupancy conditionin the area may be implemented as part of the RTOS, as part of thelighting application 927, as a standalone application program, or asother instructions in the memory.

FIG. 10 is a block diagram of a sensor type element, 1015 that operatesin and communicates via the lighting system 702 of FIG. 7. Sensor typeelement is an integrated sensor detector that generally includes a powersupply 1005 driven by a power source 1000. Power supply 805 receivespower from the power source 1000, such as an AC mains, battery, solarpanel, or any other AC or DC source. Power supply 1005 may include amagnetic transformer, electronic transformer, switching converter,rectifier, or any other similar type of circuit to convert an inputpower signal into a power signal suitable for the sensor type element1015.

The sensor type element 1015 includes power distribution circuitry 1025and a micro-control unit (MCU) 1030. As shown, MCU 1030 includes amemory 1022 (volatile and non-volatile) and a central processing unit(CPU) 1023. The memory 1022 includes a lighting application 1027 (whichcan be firmware) for both occupancy detection and lighting controloperations. The power distribution circuitry 1925 distributes power andground voltages to the MCU 1030, wireless transmitter 1008 and wirelessreceiver 1010, to provide reliable operation of the various circuitry onthe sensor type element 1015 chip.

The sensor type element 1015 also includes a wireless radiocommunication interface system configured for two way wirelesscommunication on at least one band. Optionally, the wireless radiocommunication interface system may be a dual-band system. It should beunderstood that “dual-band” means communications over two separate RFbands. The communication over the two separate RF bands can occursimultaneously (concurrently); however, it should be understood that thecommunication over the two separate RF bands may not actually occursimultaneously.

In our example, the sensor type element 1015 has a radio transmitter1008 as well as radio receiver 1010 together forming a radiotransceiver. The wireless transmitter 1008 transmits RF signals on thelighting network. This wireless transmitter 1008 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationhand. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the luminaire sensor type element 1015 may have a radio setforming a second transceiver (shown in dotted lines, transmitter andreceiver not separately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 1030 may be a system on a chip. Alternatively, a system on achip may include the transmitter 1008 and the receiver 1010 as well asthe circuitry of the MCU 830.

As shown, the sensor type element 1015 includes a drive/sense circuitry1035, such as an application firmware, drives the occupancy, daylight,audio, and photo sensor hardware. The drive/sense circuitry 1035 detectsstate changes (such as change of occupancy, audio or daylight) viasensor detector(s) 1065, such as occupancy, audio, daylight, temperatureor other environment related sensors. Sensors 1065 may be based onAcuity Brands Lighting's commercially available xPoint® Wireless ES7product.

Also as shown, the MCU 1030 includes programming in the memory 1022. Aportion of the programming configures the CPU (processor) 1023 to detectone of an occupancy or non-occupancy condition in an area in thelighting network, including the communications over one or moredifferent wireless communication bands. The programming in the memory1022 includes a real-time operating system (RTOS) and further includes alighting application 1027 which is firmware/software that engages incommunications with controlling of the light source based on one of theoccupancy or non-occupancy condition detected by the CPU 1023. Thelighting application 1027 programming in the memory 1022 carries outlighting control operations over the lighting system 702 of FIG. 7. Theprogramming for the detection of an occupancy or non-occupancy conditionin the area may be implemented as part of the RTOS, as part of thelighting application 1027, as a standalone application program, or asother instructions in the memory.

FIG. 11 is a block diagram of a plug load controller type element (plugload element) 1115 that operates in and communicates via the lightingsystem 702 of FIG. 7. In one example, plug load element 1115 is anintegrated switchable power connector that generally includes a powersupply 1105 driven by a power source 1100. Power supply 1105 receivespower from the power source 1100, such as an AC mains, battery, solarpanel, or any other AC or DC source. Power supply 1105 may include amagnetic transformer, electronic transformer, switching converter,rectifier, or any other similar type of circuit to convert an inputpower signal into a power signal suitable for the plug load element1115.

Plug load element 1115 includes an intelligent LED driver circuit 1110,coupled to LED (s) 1120 and drives that LED light source (LED) byregulating the power to LEI) 1120 by providing a constant quantity orpower to LED 1120 as its electrical properties change with temperature,for example. The intelligent LED driver circuit 1110 includes a drivercircuit that provides power to LEI) 1120 and a pilot LEI) 1117.Intelligent LED driver circuit 1110 may be a constant-voltage driver,constant-current driver, or AC LED driver type circuit that providesdimming through a pulse width modulation circuit and may have manychannels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 1110is manufactured by EldoLED.

LED driver circuit 1110 can further include an AC or DC current sourceor voltage source, a regulator, an amplifier (such as a linear amplifieror switching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 1110outputs a variable voltage or current to the LED light source 1120 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

The plug load element 1115 includes power distribution circuitry 1125and a micro-control unit (MCI) 1130. As shown, MCI 1130 is coupled toLED driver circuit 1110 and controls the light source operation of theLED 1120. MCU 1130 includes a memory 1122 (volatile and non-volatile)and a central processing unit (CPU) 1123. The memory 1122 includes alighting application 1127 (which can be firmware) for both occupancydetection and lighting control operations. The power distributioncircuitry 1125 distributes power and ground voltages to the MCU 1130,wireless transmitter 1108 and wireless receiver 1110, to providereliable operation of the various circuitry on the plug load control1115 chip.

The plug load element 1115 also includes a wireless radio communicationinterface system configured for two way wireless communication on atleast one band. Optionally, the wireless radio communication interfacesystem may be a dual-band system. It should be understood that“dual-band” means communications over two separate RF bands. Thecommunication over the two separate RF bands can occur simultaneously(concurrently); however, it should be understood that the communicationover the two separate RF bands may not actually occur simultaneously.

In our example, the plug load element 1115 has a radio set that includesradio transmitter 1108 as well as a radio receiver 1110 forming a radiotransceiver. The wireless transmitter 1108 transmits RF signals on thelighting network. This wireless transmitter 1108 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationband. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the plug load element 1115 may have a radio set forming asecond transceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 1130 may be a system on a chip. Alternatively, a system on achip may include the transmitter 1108 and the receiver 1110 as well asthe circuitry of the MCU 1130.

Plug load element 1115 plugs into existing AC wall outlets, for example,and allows existing wired lighting devices, such as table lamps or floorlamps that plug into a wall outlet, to operate in the lighting system.The plug load element 1115 instantiates the table lamp or floor lamp byallowing for commissioning and maintenance operations and processeswireless lighting controls in order to the allow the lighting device tooperate in the lighting system. Plug load element 1115 further comprisesan AC power relay 1160 which relays incoming AC power from power source1100 to other devices that may plug into the receptacle of plug loadelement 1115 thus providing an AC power outlet 1170.

Also, as shown, the MCU 1130 includes programming in the memory 1122. Aportion of the programming configures the CPU (processor) 1123 to detectone of an occupancy or non-occupancy condition in an area in thelighting network, including the communications over one or more wirelesscommunication bands. The programming in the memory 1122 includes areal-time operating system (RTOS) and further includes a lightingapplication 1127 which is firmware/software that engages incommunications with controlling of the light source based on one of theoccupancy or non-occupancy condition detected by the CPU 1123. As shown,a drive/sense circuitry detects a state change event. The lightingapplication 1127 programming in the memory 1122 carries out lightingcontrol operations over the lighting system 702 of FIG. 7. Theprogramming for the detection of an occupancy or non-occupancy conditionin the area may be implemented as part of the RTOS, as part of thelighting application 1127, as a standalone application program, or asother instructions in the memory.

Aspects of heuristic methods of detecting occupancy and non-occupancycondition in a lighting system as described above may be embodied inprogramming, e.g. in the form of software, firmware, or microcodeexecutable by a processor of any one or more of the lighting systemnodes, or by a processor of a portable handheld device, a user computersystem, a server computer or other programmable device in communicationwith one or more nodes of the lighting system. Program aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of executable code and/or associated data that iscarried on or embodied in a type of machine readable medium. “Storage”type media include any or all of the tangible memory of the computers,processors or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide non-transitory storage at any time for the software programming.All or portions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into a platform such as one of the controllersof FIGS. 2, 4, 7-10. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to one or moreof “non-transitory,” “tangible” or “storage” media, terms such ascomputer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine readable medium may take many forms, including but notlimited to, a tangible or non-transitory storage medium, a carrier wavemedium or physical transmission medium. Non-volatile storage mediainclude, for example, optical or magnetic disks, such as any of thestorage hardware in any computer(s), portable user devices or the like,such as may be used. Volatile storage media include dynamic memory, suchas main memory of such a computer or other hardware platform. Tangibletransmission media include coaxial cables; copper wire and fiber optics,including the wires that comprise a bus within a computer system.Carrier-wave transmission media can take the form of electric orelectromagnetic signals, or acoustic or light waves such as thosegenerated during radio frequency (RF) and light-based datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge (the preceding computer-readablemedia being “non-transitory” and “tangible” storage media), a carrierwave transporting data or instructions, cables or links transportingsuch a carrier wave, or any other medium from which a computer can readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying data and/or one or more sequences ofone or more instructions to a processor for execution.

Program instructions may comprise a software or firmware implementationencoded in any desired language. Programming instructions, when embodiedin a machine readable medium accessible to a processor of a computersystem or device, render a computer system or a device into aspecial-purpose machine that is customized to perform the operationsspecified in the program instructions.

Unless otherwise stated, any and all measurements, values, ratings,positions, magnitudes, sizes, and other specifications that are setforth in this specification, including in the claims that follow, areapproximate, not exact. They are intended to have a reasonable rangethat is consistent with the functions to which they relate and with whatis customary in the art to which they pertain. For example, unlessexpressly stated otherwise, a parameter value or the like may vary by asmuch as ±10% from the stated amount.

The scope of protection is limited solely by the claims that now follow.That scope is intended and should be interpreted to be as broad as isconsistent with the ordinary meaning of the language that is used in theclaims when interpreted in light of this specification and theprosecution history that follows and to encompass all structural andfunctional equivalents. Notwithstanding, none of the claims are intendedto embrace subject matter that fails to satisfy the requirement ofSections 101, 102, or 103 of the Patent Act, nor should they beinterpreted in such a way. Any unintended embracement of such subjectmatter is hereby disclaimed.

Except as stated immediately above, nothing that has been stated orillustrated is intended or should be interpreted to cause a dedicationof any component, step, feature, object, benefit, advantage, orequivalent to the public, regardless of whether it is or is not recitedin the claims.

It will be understood that the terms and expressions used herein havethe ordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”“includes”, “including” or any other variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that comprises a list of elements does not include onlythose elements but may include other elements not expressly listed orinherent to such process, method, article, or apparatus. An elementpreceded by “a” or “an” does not, without further constraints, precludethe existence of additional identical elements in the process, method,article, or apparatus that comprises the element.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

While the foregoing has described what are considered to be the bestmode and/or other examples, it is understood that various modificationsmay be made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that they may be appliedin numerous applications, only some of which have been described herein.It is intended by the following claims to claim any and allmodifications and variations that fall within the true scope of thepresent concepts.

The invention claimed is:
 1. A lighting system comprising: a lightsource; a plurality of wireless communication transmitters for wirelessradio frequency (RF) spectrum transmissions in an area, including RFspectrum transmission of at a plurality of times; a plurality ofwireless communication receivers configured to receive RF spectrumsignals of transmissions from each of the plurality of transmittersthrough the area at the plurality of times, wherein each of theplurality of the receivers is configured to generate an indicator dataof a signal characteristic of the received RF spectrum signal receivedfrom each of the transmitters at each of the plurality of times, whereinthe indicator data is one of a relative signal strength indicator (RSSI)data, bit error rate data, packet error rate data or a phase changedata, or a combination of two or more thereof; and a control modulecoupled to the light source and coupled to obtain the indicator data ofRF spectrum signals generated at each of the plurality of times fromeach of the plurality of receivers, wherein the control module isconfigured to: at each respective one of the plurality of times: (i) foreach respective one of the receivers: (a) apply one of a plurality ofheuristic algorithm coefficients (coefficients) to the indicator data ofsignals received from the transmitters, generated by the respectivereceiver, for the respective time, and (b) based on results of theapplication of the coefficients to the indicator data, generate anindicator data metric value for the indicator data generated by therespective receiver for the respective time, (ii) process the indicatordata metric values from the plurality of receivers at the respectivetime, to compute an output value at the respective time, (iii) comparethe output value at the respective time with a threshold to detect oneof a one of an occupancy condition or a non-occupancy condition in thearea, and (iv) control the light source in response to the detected oneof the occupancy condition or the non-occupancy condition in the area ateach of the plurality of times.
 2. The lighting system of claim 1wherein: the plurality of wireless transmitters are one of a WiFi, bluetooth low energy, Zigbee or ultra wide band transmitters; and theplurality of wireless receivers are one of the WiFi, blue tooth lowenergy, Zigbee or ultra wide band receivers.
 3. The lighting system ofclaim 1 wherein the control module is further configured to control thelight source in response to the detected one of the occupancy conditionor the non-occupancy condition in the area at each of the plurality oftimes.
 4. The lighting system of claim 1 further comprising a trusteddetector, wherein the trusted detector is configured to generate a knownoccupancy value for the occupancy condition or a known non-occupancyvalue for the non-occupancy condition in the area for each of theplurality of times.
 5. The lighting system of claim 4 wherein thecontrol module is further configured to, at each respective one of theplurality of times, determine a relationship of the detected one of theoccupancy condition or the non-occupancy condition in the area with theknown occupancy value or the known non-occupancy value generated by thetrusted detector during the respective one of the plurality of times. 6.The lighting system of claim 5 further comprising a learning modulecoupled to the control module, wherein the learning module is configuredto: determine whether the plurality of the coefficients are optimizedcoefficients at each of the plurality of times based on the determinedrelationship during the plurality of times.
 7. The lighting system ofclaim 6 wherein upon determination of the plurality of the coefficientsas the optimized coefficients, the learning module is configured toinstruct the control module to utilize the optimized coefficients toapply to each indicator data generated by each of the plurality ofreceivers for detection of the occupancy condition or the non-occupancycondition in the area in real time.
 8. The lighting system of claim 6wherein upon determination of the plurality of the coefficients as notthe optimized coefficients, the learning module is configured to updateone or more of the plurality of coefficients and instruct the controlmodule to utilize the updated one or more coefficients in a next time.9. The lighting system of claim 8 wherein the control module to: at eachrespective one of a plurality of times after the update: (i) for eachrespective one of the receivers: (a) apply coefficients to eachindicator data from the respective receiver, for the respective timeafter the update, and (b) based on results of the application of thecoefficients including the one or more updated coefficients to theindicator data, generate an updated indicator data metric value for theindicator data generated by the respective receiver for the respectivetime after the update, and (ii) process each of the updated indicatordata metric for each of the indicator data to compute an updated outputvalue for the plurality of the receivers, and (iii) compare the updatedoutput value at each of the respective time after the update with thethreshold to detect one of a one of an occupancy condition or anon-occupancy condition in the area.
 10. The lighting system of claim 1,wherein the control module is configured to: at each respective one ofthe plurality of times: (i) for each respective one of the receivers:(a) apply one of an another plurality of heuristic algorithmcoefficients (coefficients) to the indicator data of signals receivedfrom the transmitters, generated by the respective receiver, for therespective time, wherein the another coefficients are different from thecoefficients, and (b) based on results of the application of the anothercoefficients to the indicator data, generate an another indicator datametric value for the indicator data generated by the respective receiverfor the respective time, wherein the another indicator data metric valueis different from the indicator data metric value, (ii) process theanother indicator data metric values to compute an another output valuefor the plurality of the receivers, wherein the another output value isdifferent from the another output value; and (iii) compare the anotheroutput value at the respective time with an another threshold to detectone of a one of an occupancy condition or a non-occupancy condition of aregion in the area.
 11. The lighting system of claim 1 wherein: theindicator data is the relative signal strength indicator (RSSI) data,the plurality of coefficients comprise a set of coefficients and anindependent coefficients and the control module utilizes a logisticregression technique, wherein to apply a plurality of the coefficientsto the RSSI data, the control module to: compute a product value of oneof a plurality of coefficients with the corresponding RSSI data togenerate a RSSI data metric value for each of the RSSI data from eachrespective one of the receivers.
 12. The lighting system of claim 11wherein to compute the output value, the control module to: add each ofthe product values for each respective one of the receivers and theindependent coefficient to compute a single added value, wherein theindependent coefficient is different from each of the plurality ofcoefficients; compute an exponent of the single added value to generatean exponent value; add a digit of 1 to the exponent value to generate anadded exponent value; and divide a digit of 1 with the added exponentvalue.
 13. The lighting system of claim 1, wherein: the control moduleis a neural network module, the neural network module comprise: an inputlayer having a plurality of input nodes, each of the plurality of inputnodes include an indicator data among the plurality of indicator data; amiddle layer having a plurality of middle nodes, each of the middlenodes is coupled to each of the plurality of input nodes; and an outputnode coupled to each of the plurality of middle nodes.
 14. The lightingsystem of claim 13, wherein the indicator data is the relative signalstrength indicator (RSSI) data and the plurality of coefficientscomprise a set of weights and a set of bias constants, wherein to applya plurality of the coefficients to the RSSI data, the neural networkmodule to apply a forward propagation function, wherein the forwardpropagation function comprise: at each of the plurality of middle nodes:receive from each of the input nodes among the plurality of input nodes,a corresponding RSSI data among the plurality of RSSI data; apply a setof weights and a set of bias constants to each of the RSSI data, whereinto apply, each of the plurality of middle nodes to: compute a productvalue of each weight among the set of weights with one of the RSSI dataamong the plurality of the RSSI data to generate a RSSI data metricvalue for each of the RSSI data from each respective one of thereceivers, wherein the weight is a connection between an input node anda corresponding middle node; add each product value with a correspondingbias constant among the set of bias constants to generate a plurality ofconstant values; and sum each of the plurality constant values togenerate the single propagation value.
 15. The lighting of claim 14wherein to compute the output value, the neural network module to: atthe output node: apply an activation function to the single propagationvalue.
 16. The lighting system of claim 15 wherein the neural networkmodule to: update one or more weights among the set of weights togenerate updated set of weights.
 17. The lighting system of claim 16wherein the neural network module to: update one or more bias constantsamong the set of bias constants to generate updated set of biasconstants.
 18. The lighting system of claim 17 wherein the neuralnetwork module to: apply a backward propagation function utilizing oneof the updated weights or the updated bias constants, wherein thebackward propagation function comprise: provide, at the output node, oneof the updated weights or the updated bias constants; and apply, at eachof the plurality of middle nodes, one of the updated weights or theupdated bias constants.
 19. The lighting system of claim 17 wherein theneural network module to: apply a backward propagation functionutilizing the updated weights and the updated bias constants, whereinthe backward propagation function comprise: provide, at the output node,the updated weights and the updated bias constants; and apply, at eachof the plurality of middle nodes, the updated weights and the updatedbias constants.
 20. The lighting system of claim 1 wherein the controlmodule to detect one of the occupancy condition or the non-occupancycondition in a sub-area within the area.
 21. The lighting system ofclaim 20 wherein the control module to reject the indicator data of RFspectrum signals generated by a receiver among the plurality ofreceivers located outside of the sub-area and within the area.
 22. Thelighting system of claim 20 wherein the control module to reject theindicator data generated by a receiver among the plurality of receiversof the RF spectrum signals received from a transmitter among theplurality of transmitters located outside of the sub-area and within thearea.
 23. The lighting system of claim 1 further comprising a learningmodule coupled to the control module, wherein the learning module isconfigured to: determine a parameter based on the output value; andadjust the indicator data of the RF spectrum signal generated by atleast one of the plurality of receivers using the parameter.
 24. Amethod comprising steps of: obtaining, in a lighting system, anindicator data generated at each of a plurality of times from each of aplurality of receivers configured to receive radio frequency (RF)spectrum signals from each of a plurality of RF transmitters in an area,wherein the indicator data is one of a relative signal strengthindicator (RSSI) data, bit error rate data, packet error rate data or aphase change data, or a combination of two or more thereof, at eachrespective one of the plurality of times in the lighting system:applying a plurality of heuristic algorithm coefficients (coefficients)to each of the indicator data from each of the plurality of receiversfor the respective time, based on results of the applications of thecoefficients to indicator data, generating an indicator data metricvalue for each of the indicator data from each of the plurality ofreceivers for the respective time, and processing the indicator datametric values from the plurality of receivers at the respective time tocompute an output value at the respective time, comparing the outputvalue at the respective time with a threshold to detect one of anoccupancy condition or a non-occupancy condition in the area, andcontrolling the light source in response to the detected one of theoccupancy condition or the non-occupancy condition in the area at eachof the plurality of times.
 25. The method of claim 24 wherein thecomparing step comprises determining, at each of the respective one ofthe plurality of times, a relationship of the detected one of theoccupancy condition or the non-occupancy condition in the area with aknown occupancy value for the occupancy condition or a knownnon-occupancy value for the non-occupancy condition during therespective one of the plurality of times.
 26. The method of claim 25further comprising determining whether the plurality of the coefficientsare optimized coefficients at each of the plurality of times based onthe determined relationship during the plurality of times.
 27. Themethod of claim 26 wherein upon determination of the plurality of thecoefficients as the optimized coefficients, utilizing the optimizedcoefficients to apply to each indicator data for detection of theoccupancy condition or the non-occupancy condition in the area in realtime.
 28. The method of claim 26 wherein upon determination of theplurality of the coefficients as not the optimized coefficients,updating one or more of the plurality of coefficients.
 29. The method ofclaim 26 wherein at each respective one of the plurality of times afterthe update: applying the coefficients including the one or more updatedcoefficients to each indicator data from each of the plurality ofreceivers for the respective time after the update, based on results ofthe applications of the coefficients including the one or more updatedcoefficients to indicator data, generating an updated indicator datametric value for each of the indicator data from each of the pluralityof receivers for the respective time after the update, and processingeach of the updated indicator data metric value for each of theindicator data to compute an updated output value; and comparing theupdated output value at each of the plurality of times after the updatewith the threshold to detect one of an occupancy condition or anon-occupancy condition in the area.
 30. The method of claim 24 furthercomprising: determining a parameter based on the output value; andadjusting the indicator data of the RF spectrum signal obtained from atleast one of the plurality of receivers using the parameter.